| { | |
| "1810.04805": { | |
| "arxivId": "1810.04805", | |
| "title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" | |
| }, | |
| "2005.14165": { | |
| "arxivId": "2005.14165", | |
| "title": "Language Models are Few-Shot Learners" | |
| }, | |
| "1910.10683": { | |
| "arxivId": "1910.10683", | |
| "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" | |
| }, | |
| "1908.10084": { | |
| "arxivId": "1908.10084", | |
| "title": "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks" | |
| }, | |
| "1910.13461": { | |
| "arxivId": "1910.13461", | |
| "title": "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" | |
| }, | |
| "2307.09288": { | |
| "arxivId": "2307.09288", | |
| "title": "Llama 2: Open Foundation and Fine-Tuned Chat Models" | |
| }, | |
| "2204.02311": { | |
| "arxivId": "2204.02311", | |
| "title": "PaLM: Scaling Language Modeling with Pathways" | |
| }, | |
| "2107.03374": { | |
| "arxivId": "2107.03374", | |
| "title": "Evaluating Large Language Models Trained on Code" | |
| }, | |
| "2101.00190": { | |
| "arxivId": "2101.00190", | |
| "title": "Prefix-Tuning: Optimizing Continuous Prompts for Generation" | |
| }, | |
| "2004.04906": { | |
| "arxivId": "2004.04906", | |
| "title": "Dense Passage Retrieval for Open-Domain Question Answering" | |
| }, | |
| "1909.01066": { | |
| "arxivId": "1909.01066", | |
| "title": "Language Models as Knowledge Bases?" | |
| }, | |
| "1704.00051": { | |
| "arxivId": "1704.00051", | |
| "title": "Reading Wikipedia to Answer Open-Domain Questions" | |
| }, | |
| "2002.08909": { | |
| "arxivId": "2002.08909", | |
| "title": "REALM: Retrieval-Augmented Language Model Pre-Training" | |
| }, | |
| "1902.07243": { | |
| "arxivId": "1902.07243", | |
| "title": "Graph Neural Networks for Social Recommendation" | |
| }, | |
| "2210.03629": { | |
| "arxivId": "2210.03629", | |
| "title": "ReAct: Synergizing Reasoning and Acting in Language Models" | |
| }, | |
| "2302.04761": { | |
| "arxivId": "2302.04761", | |
| "title": "Toolformer: Language Models Can Teach Themselves to Use Tools" | |
| }, | |
| "2202.12837": { | |
| "arxivId": "2202.12837", | |
| "title": "Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?" | |
| }, | |
| "2101.06804": { | |
| "arxivId": "2101.06804", | |
| "title": "What Makes Good In-Context Examples for GPT-3?" | |
| }, | |
| "2004.12832": { | |
| "arxivId": "2004.12832", | |
| "title": "ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT" | |
| }, | |
| "2007.01282": { | |
| "arxivId": "2007.01282", | |
| "title": "Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering" | |
| }, | |
| "2312.10997": { | |
| "arxivId": "2312.10997", | |
| "title": "Retrieval-Augmented Generation for Large Language Models: A Survey" | |
| }, | |
| "1904.02232": { | |
| "arxivId": "1904.02232", | |
| "title": "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis" | |
| }, | |
| "2208.03299": { | |
| "arxivId": "2208.03299", | |
| "title": "Few-shot Learning with Retrieval Augmented Language Models" | |
| }, | |
| "2112.08633": { | |
| "arxivId": "2112.08633", | |
| "title": "Learning To Retrieve Prompts for In-Context Learning" | |
| }, | |
| "1702.01932": { | |
| "arxivId": "1702.01932", | |
| "title": "A Knowledge-Grounded Neural Conversation Model" | |
| }, | |
| "2207.05221": { | |
| "arxivId": "2207.05221", | |
| "title": "Language Models (Mostly) Know What They Know" | |
| }, | |
| "2104.07567": { | |
| "arxivId": "2104.07567", | |
| "title": "Retrieval Augmentation Reduces Hallucination in Conversation" | |
| }, | |
| "2301.12652": { | |
| "arxivId": "2301.12652", | |
| "title": "REPLUG: Retrieval-Augmented Black-Box Language Models" | |
| }, | |
| "2211.17192": { | |
| "arxivId": "2211.17192", | |
| "title": "Fast Inference from Transformers via Speculative Decoding" | |
| }, | |
| "2302.00083": { | |
| "arxivId": "2302.00083", | |
| "title": "In-Context Retrieval-Augmented Language Models" | |
| }, | |
| "2310.11511": { | |
| "arxivId": "2310.11511", | |
| "title": "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection" | |
| }, | |
| "1612.04426": { | |
| "arxivId": "1612.04426", | |
| "title": "Improving Neural Language Models with a Continuous Cache" | |
| }, | |
| "2106.01760": { | |
| "arxivId": "2106.01760", | |
| "title": "Template-Based Named Entity Recognition Using BART" | |
| }, | |
| "2209.10063": { | |
| "arxivId": "2209.10063", | |
| "title": "Generate rather than Retrieve: Large Language Models are Strong Context Generators" | |
| }, | |
| "2212.10509": { | |
| "arxivId": "2212.10509", | |
| "title": "Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions" | |
| }, | |
| "2107.07566": { | |
| "arxivId": "2107.07566", | |
| "title": "Internet-Augmented Dialogue Generation" | |
| }, | |
| "2302.01318": { | |
| "arxivId": "2302.01318", | |
| "title": "Accelerating Large Language Model Decoding with Speculative Sampling" | |
| }, | |
| "2004.10645": { | |
| "arxivId": "2004.10645", | |
| "title": "AmbigQA: Answering Ambiguous Open-domain Questions" | |
| }, | |
| "2110.07904": { | |
| "arxivId": "2110.07904", | |
| "title": "SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer" | |
| }, | |
| "2012.04584": { | |
| "arxivId": "2012.04584", | |
| "title": "Distilling Knowledge from Reader to Retriever for Question Answering" | |
| }, | |
| "2306.13063": { | |
| "arxivId": "2306.13063", | |
| "title": "Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs" | |
| }, | |
| "2203.11147": { | |
| "arxivId": "2203.11147", | |
| "title": "Teaching language models to support answers with verified quotes" | |
| }, | |
| "2107.07567": { | |
| "arxivId": "2107.07567", | |
| "title": "Beyond Goldfish Memory: Long-Term Open-Domain Conversation" | |
| }, | |
| "2005.04611": { | |
| "arxivId": "2005.04611", | |
| "title": "How Context Affects Language Models' Factual Predictions" | |
| }, | |
| "2212.14024": { | |
| "arxivId": "2212.14024", | |
| "title": "Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP" | |
| }, | |
| "2209.01975": { | |
| "arxivId": "2209.01975", | |
| "title": "Selective Annotation Makes Language Models Better Few-Shot Learners" | |
| }, | |
| "2209.14610": { | |
| "arxivId": "2209.14610", | |
| "title": "Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning" | |
| }, | |
| "2307.02046": { | |
| "arxivId": "2307.02046", | |
| "title": "Recommender Systems in the Era of Large Language Models (LLMs)" | |
| }, | |
| "1202.6101": { | |
| "arxivId": "1202.6101", | |
| "title": "Maximum inner-product search using cone trees" | |
| }, | |
| "2212.10496": { | |
| "arxivId": "2212.10496", | |
| "title": "Precise Zero-Shot Dense Retrieval without Relevance Labels" | |
| }, | |
| "2107.06641": { | |
| "arxivId": "2107.06641", | |
| "title": "Trustworthy AI: A Computational Perspective" | |
| }, | |
| "2203.08913": { | |
| "arxivId": "2203.08913", | |
| "title": "Memorizing Transformers" | |
| }, | |
| "2212.02437": { | |
| "arxivId": "2212.02437", | |
| "title": "In-context Examples Selection for Machine Translation" | |
| }, | |
| "2006.15020": { | |
| "arxivId": "2006.15020", | |
| "title": "Pre-training via Paraphrasing" | |
| }, | |
| "1906.05807": { | |
| "arxivId": "1906.05807", | |
| "title": "Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index" | |
| }, | |
| "2106.05346": { | |
| "arxivId": "2106.05346", | |
| "title": "End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering" | |
| }, | |
| "2004.07202": { | |
| "arxivId": "2004.07202", | |
| "title": "Entities as Experts: Sparse Memory Access with Entity Supervision" | |
| }, | |
| "1911.02707": { | |
| "arxivId": "1911.02707", | |
| "title": "Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs" | |
| }, | |
| "2305.06983": { | |
| "arxivId": "2305.06983", | |
| "title": "Active Retrieval Augmented Generation" | |
| }, | |
| "2108.11601": { | |
| "arxivId": "2108.11601", | |
| "title": "Retrieval Augmented Code Generation and Summarization" | |
| }, | |
| "2211.05110": { | |
| "arxivId": "2211.05110", | |
| "title": "Large Language Models with Controllable Working Memory" | |
| }, | |
| "2205.12674": { | |
| "arxivId": "2205.12674", | |
| "title": "Training Language Models with Memory Augmentation" | |
| }, | |
| "2305.15294": { | |
| "arxivId": "2305.15294", | |
| "title": "Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy" | |
| }, | |
| "2310.01558": { | |
| "arxivId": "2310.01558", | |
| "title": "Making Retrieval-Augmented Language Models Robust to Irrelevant Context" | |
| }, | |
| "2203.05115": { | |
| "arxivId": "2203.05115", | |
| "title": "Internet-augmented language models through few-shot prompting for open-domain question answering" | |
| }, | |
| "2301.13808": { | |
| "arxivId": "2301.13808", | |
| "title": "Large Language Models are Versatile Decomposers: Decomposing Evidence and Questions for Table-based Reasoning" | |
| }, | |
| "2207.05987": { | |
| "arxivId": "2207.05987", | |
| "title": "DocPrompting: Generating Code by Retrieving the Docs" | |
| }, | |
| "2204.02849": { | |
| "arxivId": "2204.02849", | |
| "title": "KNN-Diffusion: Image Generation via Large-Scale Retrieval" | |
| }, | |
| "2212.10789": { | |
| "arxivId": "2212.10789", | |
| "title": "Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing" | |
| }, | |
| "2102.02557": { | |
| "arxivId": "2102.02557", | |
| "title": "Adaptive Semiparametric Language Models" | |
| }, | |
| "2109.04212": { | |
| "arxivId": "2109.04212", | |
| "title": "Efficient Nearest Neighbor Language Models" | |
| }, | |
| "2304.01116": { | |
| "arxivId": "2304.01116", | |
| "title": "ReMoDiffuse: Retrieval-Augmented Motion Diffusion Model" | |
| }, | |
| "2310.08319": { | |
| "arxivId": "2310.08319", | |
| "title": "Fine-Tuning LLaMA for Multi-Stage Text Retrieval" | |
| }, | |
| "2402.19473": { | |
| "arxivId": "2402.19473", | |
| "title": "Retrieval-Augmented Generation for AI-Generated Content: A Survey" | |
| }, | |
| "2305.04320": { | |
| "arxivId": "2305.04320", | |
| "title": "Unified Demonstration Retriever for In-Context Learning" | |
| }, | |
| "2302.05698": { | |
| "arxivId": "2302.05698", | |
| "title": "Compositional Exemplars for In-context Learning" | |
| }, | |
| "2108.05552": { | |
| "arxivId": "2108.05552", | |
| "title": "Graph Trend Filtering Networks for Recommendation" | |
| }, | |
| "2310.01352": { | |
| "arxivId": "2310.01352", | |
| "title": "RA-DIT: Retrieval-Augmented Dual Instruction Tuning" | |
| }, | |
| "2210.02627": { | |
| "arxivId": "2210.02627", | |
| "title": "Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering" | |
| }, | |
| "2005.08147": { | |
| "arxivId": "2005.08147", | |
| "title": "Attacking Black-box Recommendations via Copying Cross-domain User Profiles" | |
| }, | |
| "2212.05221": { | |
| "arxivId": "2212.05221", | |
| "title": "Reveal: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory" | |
| }, | |
| "2209.14290": { | |
| "arxivId": "2209.14290", | |
| "title": "FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation" | |
| }, | |
| "2305.14002": { | |
| "arxivId": "2305.14002", | |
| "title": "Improving Language Models via Plug-and-Play Retrieval Feedback" | |
| }, | |
| "2112.07708": { | |
| "arxivId": "2112.07708", | |
| "title": "Learning to Retrieve Passages without Supervision" | |
| }, | |
| "2106.00957": { | |
| "arxivId": "2106.00957", | |
| "title": "RevCore: Review-Augmented Conversational Recommendation" | |
| }, | |
| "2209.15323": { | |
| "arxivId": "2209.15323", | |
| "title": "Smallcap: Lightweight Image Captioning Prompted with Retrieval Augmentation" | |
| }, | |
| "2207.06300": { | |
| "arxivId": "2207.06300", | |
| "title": "Re2G: Retrieve, Rerank, Generate" | |
| }, | |
| "2305.02437": { | |
| "arxivId": "2305.02437", | |
| "title": "Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory" | |
| }, | |
| "2206.08082": { | |
| "arxivId": "2206.08082", | |
| "title": "Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator" | |
| }, | |
| "2210.17236": { | |
| "arxivId": "2210.17236", | |
| "title": "When Language Model Meets Private Library" | |
| }, | |
| "2304.06762": { | |
| "arxivId": "2304.06762", | |
| "title": "Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study" | |
| }, | |
| "2310.04027": { | |
| "arxivId": "2310.04027", | |
| "title": "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models" | |
| }, | |
| "2303.08518": { | |
| "arxivId": "2303.08518", | |
| "title": "UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation" | |
| }, | |
| "2212.01349": { | |
| "arxivId": "2212.01349", | |
| "title": "Nonparametric Masked Language Modeling" | |
| }, | |
| "2310.15141": { | |
| "arxivId": "2310.15141", | |
| "title": "SpecTr: Fast Speculative Decoding via Optimal Transport" | |
| }, | |
| "2207.13162": { | |
| "arxivId": "2207.13162", | |
| "title": "Retrieval-Augmented Transformer for Image Captioning" | |
| }, | |
| "2207.10307": { | |
| "arxivId": "2207.10307", | |
| "title": "Knowledge-enhanced Black-box Attacks for Recommendations" | |
| }, | |
| "2209.10117": { | |
| "arxivId": "2209.10117", | |
| "title": "A Comprehensive Survey on Trustworthy Recommender Systems" | |
| }, | |
| "2304.14732": { | |
| "arxivId": "2304.14732", | |
| "title": "Search-in-the-Chain: Towards the Accurate, Credible and Traceable Content Generation for Complex Knowledge-intensive Tasks" | |
| }, | |
| "2305.18846": { | |
| "arxivId": "2305.18846", | |
| "title": "Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation" | |
| }, | |
| "2210.13693": { | |
| "arxivId": "2210.13693", | |
| "title": "XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing" | |
| }, | |
| "2401.01301": { | |
| "arxivId": "2401.01301", | |
| "title": "Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models" | |
| }, | |
| "2302.08266": { | |
| "arxivId": "2302.08266", | |
| "title": "Fairly Adaptive Negative Sampling for Recommendations" | |
| }, | |
| "2305.16171": { | |
| "arxivId": "2305.16171", | |
| "title": "Multi-lingual and Multi-cultural Figurative Language Understanding" | |
| }, | |
| "2310.14393": { | |
| "arxivId": "2310.14393", | |
| "title": "Merging Generated and Retrieved Knowledge for Open-Domain QA" | |
| }, | |
| "2211.05165": { | |
| "arxivId": "2211.05165", | |
| "title": "Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database" | |
| }, | |
| "2310.05002": { | |
| "arxivId": "2310.05002", | |
| "title": "Self-Knowledge Guided Retrieval Augmentation for Large Language Models" | |
| }, | |
| "2402.16893": { | |
| "arxivId": "2402.16893", | |
| "title": "The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)" | |
| }, | |
| "2309.10954": { | |
| "arxivId": "2309.10954", | |
| "title": "In-Context Learning for Text Classification with Many Labels" | |
| }, | |
| "2402.08416": { | |
| "arxivId": "2402.08416", | |
| "title": "Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning" | |
| }, | |
| "2210.12360": { | |
| "arxivId": "2210.12360", | |
| "title": "Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models" | |
| }, | |
| "2307.06962": { | |
| "arxivId": "2307.06962", | |
| "title": "Copy is All You Need" | |
| }, | |
| "2210.05758": { | |
| "arxivId": "2210.05758", | |
| "title": "Decoupled Context Processing for Context Augmented Language Modeling" | |
| }, | |
| "2310.18347": { | |
| "arxivId": "2310.18347", | |
| "title": "PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter" | |
| }, | |
| "2305.05181": { | |
| "arxivId": "2305.05181", | |
| "title": "MoT: Memory-of-Thought Enables ChatGPT to Self-Improve" | |
| }, | |
| "2305.19912": { | |
| "arxivId": "2305.19912", | |
| "title": "Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data" | |
| }, | |
| "1901.01474": { | |
| "arxivId": "1901.01474", | |
| "title": "Bilinear Supervised Hashing Based on 2D Image Features" | |
| }, | |
| "2402.13973": { | |
| "arxivId": "2402.13973", | |
| "title": "Linear-Time Graph Neural Networks for Scalable Recommendations" | |
| }, | |
| "2312.11361": { | |
| "arxivId": "2312.11361", | |
| "title": "NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation" | |
| }, | |
| "1706.03762": { | |
| "arxivId": "1706.03762", | |
| "title": "Attention is All you Need" | |
| }, | |
| "2203.02155": { | |
| "arxivId": "2203.02155", | |
| "title": "Training language models to follow instructions with human feedback" | |
| }, | |
| "2303.08774": { | |
| "arxivId": "2303.08774", | |
| "title": "GPT-4 Technical Report" | |
| }, | |
| "1911.02116": { | |
| "arxivId": "1911.02116", | |
| "title": "Unsupervised Cross-lingual Representation Learning at Scale" | |
| }, | |
| "2005.11401": { | |
| "arxivId": "2005.11401", | |
| "title": "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" | |
| }, | |
| "1702.08734": { | |
| "arxivId": "1702.08734", | |
| "title": "Billion-Scale Similarity Search with GPUs" | |
| }, | |
| "2205.01068": { | |
| "arxivId": "2205.01068", | |
| "title": "OPT: Open Pre-trained Transformer Language Models" | |
| }, | |
| "2104.08821": { | |
| "arxivId": "2104.08821", | |
| "title": "SimCSE: Simple Contrastive Learning of Sentence Embeddings" | |
| }, | |
| "2009.03300": { | |
| "arxivId": "2009.03300", | |
| "title": "Measuring Massive Multitask Language Understanding" | |
| }, | |
| "1905.00537": { | |
| "arxivId": "1905.00537", | |
| "title": "SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems" | |
| }, | |
| "1705.03551": { | |
| "arxivId": "1705.03551", | |
| "title": "TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension" | |
| }, | |
| "1809.09600": { | |
| "arxivId": "1809.09600", | |
| "title": "HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering" | |
| }, | |
| "2211.05100": { | |
| "arxivId": "2211.05100", | |
| "title": "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model" | |
| }, | |
| "2101.00027": { | |
| "arxivId": "2101.00027", | |
| "title": "The Pile: An 800GB Dataset of Diverse Text for Language Modeling" | |
| }, | |
| "2202.03629": { | |
| "arxivId": "2202.03629", | |
| "title": "Survey of Hallucination in Natural Language Generation" | |
| }, | |
| "1803.05355": { | |
| "arxivId": "1803.05355", | |
| "title": "FEVER: a Large-scale Dataset for Fact Extraction and VERification" | |
| }, | |
| "1603.09320": { | |
| "arxivId": "1603.09320", | |
| "title": "Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs" | |
| }, | |
| "2103.10360": { | |
| "arxivId": "2103.10360", | |
| "title": "GLM: General Language Model Pretraining with Autoregressive Blank Infilling" | |
| }, | |
| "2007.00808": { | |
| "arxivId": "2007.00808", | |
| "title": "Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval" | |
| }, | |
| "2112.09332": { | |
| "arxivId": "2112.09332", | |
| "title": "WebGPT: Browser-assisted question-answering with human feedback" | |
| }, | |
| "1811.01241": { | |
| "arxivId": "1811.01241", | |
| "title": "Wizard of Wikipedia: Knowledge-Powered Conversational agents" | |
| }, | |
| "2112.04426": { | |
| "arxivId": "2112.04426", | |
| "title": "Improving language models by retrieving from trillions of tokens" | |
| }, | |
| "1911.00172": { | |
| "arxivId": "1911.00172", | |
| "title": "Generalization through Memorization: Nearest Neighbor Language Models" | |
| }, | |
| "2204.06745": { | |
| "arxivId": "2204.06745", | |
| "title": "GPT-NeoX-20B: An Open-Source Autoregressive Language Model" | |
| }, | |
| "2306.01116": { | |
| "arxivId": "2306.01116", | |
| "title": "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only" | |
| }, | |
| "1909.06146": { | |
| "arxivId": "1909.06146", | |
| "title": "PubMedQA: A Dataset for Biomedical Research Question Answering" | |
| }, | |
| "2112.09118": { | |
| "arxivId": "2112.09118", | |
| "title": "Unsupervised Dense Information Retrieval with Contrastive Learning" | |
| }, | |
| "2009.02252": { | |
| "arxivId": "2009.02252", | |
| "title": "KILT: a Benchmark for Knowledge Intensive Language Tasks" | |
| }, | |
| "2304.03277": { | |
| "arxivId": "2304.03277", | |
| "title": "Instruction Tuning with GPT-4" | |
| }, | |
| "2009.13081": { | |
| "arxivId": "2009.13081", | |
| "title": "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams" | |
| }, | |
| "2003.06713": { | |
| "arxivId": "2003.06713", | |
| "title": "Document Ranking with a Pretrained Sequence-to-Sequence Model" | |
| }, | |
| "2212.03533": { | |
| "arxivId": "2212.03533", | |
| "title": "Text Embeddings by Weakly-Supervised Contrastive Pre-training" | |
| }, | |
| "2112.07899": { | |
| "arxivId": "2112.07899", | |
| "title": "Large Dual Encoders Are Generalizable Retrievers" | |
| }, | |
| "1910.14424": { | |
| "arxivId": "1910.14424", | |
| "title": "Multi-Stage Document Ranking with BERT" | |
| }, | |
| "2205.05131": { | |
| "arxivId": "2205.05131", | |
| "title": "UL2: Unifying Language Learning Paradigms" | |
| }, | |
| "2112.10668": { | |
| "arxivId": "2112.10668", | |
| "title": "Few-shot Learning with Multilingual Generative Language Models" | |
| }, | |
| "2010.04389": { | |
| "arxivId": "2010.04389", | |
| "title": "A Survey of Knowledge-enhanced Text Generation" | |
| }, | |
| "2202.06991": { | |
| "arxivId": "2202.06991", | |
| "title": "Transformer Memory as a Differentiable Search Index" | |
| }, | |
| "2203.14371": { | |
| "arxivId": "2203.14371", | |
| "title": "MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering" | |
| }, | |
| "1804.05936": { | |
| "arxivId": "1804.05936", | |
| "title": "Learning a Deep Listwise Context Model for Ranking Refinement" | |
| }, | |
| "2209.11755": { | |
| "arxivId": "2209.11755", | |
| "title": "Promptagator: Few-shot Dense Retrieval From 8 Examples" | |
| }, | |
| "2308.07107": { | |
| "arxivId": "2308.07107", | |
| "title": "Large Language Models for Information Retrieval: A Survey" | |
| }, | |
| "2305.18486": { | |
| "arxivId": "2305.18486", | |
| "title": "A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets" | |
| }, | |
| "2202.08904": { | |
| "arxivId": "2202.08904", | |
| "title": "SGPT: GPT Sentence Embeddings for Semantic Search" | |
| }, | |
| "2202.01110": { | |
| "arxivId": "2202.01110", | |
| "title": "A Survey on Retrieval-Augmented Text Generation" | |
| }, | |
| "2006.05009": { | |
| "arxivId": "2006.05009", | |
| "title": "Few-Shot Generative Conversational Query Rewriting" | |
| }, | |
| "2209.14491": { | |
| "arxivId": "2209.14491", | |
| "title": "Re-Imagen: Retrieval-Augmented Text-to-Image Generator" | |
| }, | |
| "2204.10628": { | |
| "arxivId": "2204.10628", | |
| "title": "Autoregressive Search Engines: Generating Substrings as Document Identifiers" | |
| }, | |
| "1804.04526": { | |
| "arxivId": "1804.04526", | |
| "title": "EventKG: A Multilingual Event-Centric Temporal Knowledge Graph" | |
| }, | |
| "2008.09093": { | |
| "arxivId": "2008.09093", | |
| "title": "PARADE: Passage Representation Aggregation forDocument Reranking" | |
| }, | |
| "2310.04408": { | |
| "arxivId": "2310.04408", | |
| "title": "RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation" | |
| }, | |
| "2312.15166": { | |
| "arxivId": "2312.15166", | |
| "title": "SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling" | |
| }, | |
| "2210.10634": { | |
| "arxivId": "2210.10634", | |
| "title": "RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses" | |
| }, | |
| "2210.02928": { | |
| "arxivId": "2210.02928", | |
| "title": "MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text" | |
| }, | |
| "2205.12035": { | |
| "arxivId": "2205.12035", | |
| "title": "RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder" | |
| }, | |
| "2302.07452": { | |
| "arxivId": "2302.07452", | |
| "title": "How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval" | |
| }, | |
| "2402.13178": { | |
| "arxivId": "2402.13178", | |
| "title": "Benchmarking Retrieval-Augmented Generation for Medicine" | |
| }, | |
| "2401.14887": { | |
| "arxivId": "2401.14887", | |
| "title": "The Power of Noise: Redefining Retrieval for RAG Systems" | |
| }, | |
| "2310.06117": { | |
| "arxivId": "2310.06117", | |
| "title": "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models" | |
| }, | |
| "2305.13269": { | |
| "arxivId": "2305.13269", | |
| "title": "Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources" | |
| }, | |
| "2202.00217": { | |
| "arxivId": "2202.00217", | |
| "title": "WebFormer: The Web-page Transformer for Structure Information Extraction" | |
| }, | |
| "2311.09476": { | |
| "arxivId": "2311.09476", | |
| "title": "ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems" | |
| }, | |
| "2310.05029": { | |
| "arxivId": "2310.05029", | |
| "title": "Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading" | |
| }, | |
| "2204.05511": { | |
| "arxivId": "2204.05511", | |
| "title": "GERE: Generative Evidence Retrieval for Fact Verification" | |
| }, | |
| "2305.17331": { | |
| "arxivId": "2305.17331", | |
| "title": "Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In" | |
| }, | |
| "2305.04757": { | |
| "arxivId": "2305.04757", | |
| "title": "Augmented Large Language Models with Parametric Knowledge Guiding" | |
| }, | |
| "2304.10453": { | |
| "arxivId": "2304.10453", | |
| "title": "Phoenix: Democratizing ChatGPT across Languages" | |
| }, | |
| "2302.04858": { | |
| "arxivId": "2302.04858", | |
| "title": "Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning" | |
| }, | |
| "2405.07437": { | |
| "arxivId": "2405.07437", | |
| "title": "Evaluation of Retrieval-Augmented Generation: A Survey" | |
| }, | |
| "2308.11761": { | |
| "arxivId": "2308.11761", | |
| "title": "KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases" | |
| }, | |
| "2306.04504": { | |
| "arxivId": "2306.04504", | |
| "title": "Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers" | |
| }, | |
| "2311.08147": { | |
| "arxivId": "2311.08147", | |
| "title": "RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge" | |
| }, | |
| "2404.05970": { | |
| "arxivId": "2404.05970", | |
| "title": "Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation" | |
| }, | |
| "2309.08051": { | |
| "arxivId": "2309.08051", | |
| "title": "Retrieval-Augmented Text-to-Audio Generation" | |
| }, | |
| "2403.05676": { | |
| "arxivId": "2403.05676", | |
| "title": "PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design" | |
| }, | |
| "2310.13682": { | |
| "arxivId": "2310.13682", | |
| "title": "Optimizing Retrieval-augmented Reader Models via Token Elimination" | |
| }, | |
| "2111.07267": { | |
| "arxivId": "2111.07267", | |
| "title": "Understanding Jargon: Combining Extraction and Generation for Definition Modeling" | |
| }, | |
| "1609.02907": { | |
| "arxivId": "1609.02907", | |
| "title": "Semi-Supervised Classification with Graph Convolutional Networks" | |
| }, | |
| "1907.11692": { | |
| "arxivId": "1907.11692", | |
| "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach" | |
| }, | |
| "1710.10903": { | |
| "arxivId": "1710.10903", | |
| "title": "Graph Attention Networks" | |
| }, | |
| "1706.02216": { | |
| "arxivId": "1706.02216", | |
| "title": "Inductive Representation Learning on Large Graphs" | |
| }, | |
| "2104.08691": { | |
| "arxivId": "2104.08691", | |
| "title": "The Power of Scale for Parameter-Efficient Prompt Tuning" | |
| }, | |
| "1707.01476": { | |
| "arxivId": "1707.01476", | |
| "title": "Convolutional 2D Knowledge Graph Embeddings" | |
| }, | |
| "2305.14314": { | |
| "arxivId": "2305.14314", | |
| "title": "QLoRA: Efficient Finetuning of Quantized LLMs" | |
| }, | |
| "1811.00937": { | |
| "arxivId": "1811.00937", | |
| "title": "CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge" | |
| }, | |
| "1911.11641": { | |
| "arxivId": "1911.11641", | |
| "title": "PIQA: Reasoning about Physical Commonsense in Natural Language" | |
| }, | |
| "1907.10903": { | |
| "arxivId": "1907.10903", | |
| "title": "DropEdge: Towards Deep Graph Convolutional Networks on Node Classification" | |
| }, | |
| "1809.02789": { | |
| "arxivId": "1809.02789", | |
| "title": "Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering" | |
| }, | |
| "2103.10385": { | |
| "arxivId": "2103.10385", | |
| "title": "GPT Understands, Too" | |
| }, | |
| "2407.21783": { | |
| "arxivId": "2407.21783", | |
| "title": "The Llama 3 Herd of Models" | |
| }, | |
| "1606.03126": { | |
| "arxivId": "1606.03126", | |
| "title": "Key-Value Memory Networks for Directly Reading Documents" | |
| }, | |
| "2307.03172": { | |
| "arxivId": "2307.03172", | |
| "title": "Lost in the Middle: How Language Models Use Long Contexts" | |
| }, | |
| "2007.08663": { | |
| "arxivId": "2007.08663", | |
| "title": "TUDataset: A collection of benchmark datasets for learning with graphs" | |
| }, | |
| "1506.02075": { | |
| "arxivId": "1506.02075", | |
| "title": "Large-scale Simple Question Answering with Memory Networks" | |
| }, | |
| "1803.06643": { | |
| "arxivId": "1803.06643", | |
| "title": "The Web as a Knowledge-Base for Answering Complex Questions" | |
| }, | |
| "2104.06378": { | |
| "arxivId": "2104.06378", | |
| "title": "QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering" | |
| }, | |
| "1711.05851": { | |
| "arxivId": "1711.05851", | |
| "title": "Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning" | |
| }, | |
| "2306.08302": { | |
| "arxivId": "2306.08302", | |
| "title": "Unifying Large Language Models and Knowledge Graphs: A Roadmap" | |
| }, | |
| "1909.02151": { | |
| "arxivId": "1909.02151", | |
| "title": "KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning" | |
| }, | |
| "1709.04071": { | |
| "arxivId": "1709.04071", | |
| "title": "Variational Reasoning for Question Answering with Knowledge Graph" | |
| }, | |
| "1809.00782": { | |
| "arxivId": "1809.00782", | |
| "title": "Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text" | |
| }, | |
| "2010.05953": { | |
| "arxivId": "2010.05953", | |
| "title": "COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs" | |
| }, | |
| "1904.09537": { | |
| "arxivId": "1904.09537", | |
| "title": "PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text" | |
| }, | |
| "2311.05232": { | |
| "arxivId": "2311.05232", | |
| "title": "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions" | |
| }, | |
| "2407.10671": { | |
| "arxivId": "2407.10671", | |
| "title": "Qwen2 Technical Report" | |
| }, | |
| "1906.07348": { | |
| "arxivId": "1906.07348", | |
| "title": "Zero-Shot Entity Linking by Reading Entity Descriptions" | |
| }, | |
| "2005.00646": { | |
| "arxivId": "2005.00646", | |
| "title": "Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering" | |
| }, | |
| "2305.09645": { | |
| "arxivId": "2305.09645", | |
| "title": "StructGPT: A General Framework for Large Language Model to Reason over Structured Data" | |
| }, | |
| "2011.07743": { | |
| "arxivId": "2011.07743", | |
| "title": "Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases" | |
| }, | |
| "2101.03737": { | |
| "arxivId": "2101.03737", | |
| "title": "Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals" | |
| }, | |
| "2105.11644": { | |
| "arxivId": "2105.11644", | |
| "title": "A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions" | |
| }, | |
| "2305.10037": { | |
| "arxivId": "2305.10037", | |
| "title": "Can Language Models Solve Graph Problems in Natural Language?" | |
| }, | |
| "2308.07134": { | |
| "arxivId": "2308.07134", | |
| "title": "Language is All a Graph Needs" | |
| }, | |
| "2310.01061": { | |
| "arxivId": "2310.01061", | |
| "title": "Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning" | |
| }, | |
| "2109.08678": { | |
| "arxivId": "2109.08678", | |
| "title": "RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering" | |
| }, | |
| "2305.15066": { | |
| "arxivId": "2305.15066", | |
| "title": "GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking" | |
| }, | |
| "2404.16130": { | |
| "arxivId": "2404.16130", | |
| "title": "From Local to Global: A Graph RAG Approach to Query-Focused Summarization" | |
| }, | |
| "2007.13069": { | |
| "arxivId": "2007.13069", | |
| "title": "A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges" | |
| }, | |
| "2307.07697": { | |
| "arxivId": "2307.07697", | |
| "title": "Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph" | |
| }, | |
| "2202.13296": { | |
| "arxivId": "2202.13296", | |
| "title": "Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering" | |
| }, | |
| "2312.02783": { | |
| "arxivId": "2312.02783", | |
| "title": "Large Language Models on Graphs: A Comprehensive Survey" | |
| }, | |
| "2311.10723": { | |
| "arxivId": "2311.10723", | |
| "title": "Large Language Models in Finance: A Survey" | |
| }, | |
| "2109.01653": { | |
| "arxivId": "2109.01653", | |
| "title": "CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge" | |
| }, | |
| "2310.11829": { | |
| "arxivId": "2310.11829", | |
| "title": "Towards Graph Foundation Models: A Survey and Beyond" | |
| }, | |
| "2108.06688": { | |
| "arxivId": "2108.06688", | |
| "title": "Complex Knowledge Base Question Answering: A Survey" | |
| }, | |
| "2102.08942": { | |
| "arxivId": "2102.08942", | |
| "title": "A Survey on Locality Sensitive Hashing Algorithms and their Applications" | |
| }, | |
| "2306.04136": { | |
| "arxivId": "2306.04136", | |
| "title": "Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering" | |
| }, | |
| "2101.00376": { | |
| "arxivId": "2101.00376", | |
| "title": "RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge" | |
| }, | |
| "2310.01089": { | |
| "arxivId": "2310.01089", | |
| "title": "GraphText: Graph Reasoning in Text Space" | |
| }, | |
| "2204.08109": { | |
| "arxivId": "2204.08109", | |
| "title": "ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering" | |
| }, | |
| "2308.11730": { | |
| "arxivId": "2308.11730", | |
| "title": "Knowledge Graph Prompting for Multi-Document Question Answering" | |
| }, | |
| "2210.01613": { | |
| "arxivId": "2210.01613", | |
| "title": "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering" | |
| }, | |
| "2308.13259": { | |
| "arxivId": "2308.13259", | |
| "title": "Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answering" | |
| }, | |
| "2212.00959": { | |
| "arxivId": "2212.00959", | |
| "title": "UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph" | |
| }, | |
| "2202.00120": { | |
| "arxivId": "2202.00120", | |
| "title": "QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers" | |
| }, | |
| "2309.11206": { | |
| "arxivId": "2309.11206", | |
| "title": "Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering" | |
| }, | |
| "2309.03118": { | |
| "arxivId": "2309.03118", | |
| "title": "Knowledge Solver: Teaching LLMs to Search for Domain Knowledge from Knowledge Graphs" | |
| }, | |
| "2305.06590": { | |
| "arxivId": "2305.06590", | |
| "title": "FactKG: Fact Verification via Reasoning on Knowledge Graphs" | |
| }, | |
| "2403.18105": { | |
| "arxivId": "2403.18105", | |
| "title": "Large Language Models for Education: A Survey and Outlook" | |
| }, | |
| "2402.08170": { | |
| "arxivId": "2402.08170", | |
| "title": "LLaGA: Large Language and Graph Assistant" | |
| }, | |
| "2405.06211": { | |
| "arxivId": "2405.06211", | |
| "title": "A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models" | |
| }, | |
| "2202.06129": { | |
| "arxivId": "2202.06129", | |
| "title": "RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph" | |
| }, | |
| "2402.11163": { | |
| "arxivId": "2402.11163", | |
| "title": "KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph" | |
| }, | |
| "2402.07197": { | |
| "arxivId": "2402.07197", | |
| "title": "GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks" | |
| }, | |
| "2404.00579": { | |
| "arxivId": "2404.00579", | |
| "title": "A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)" | |
| }, | |
| "2310.08975": { | |
| "arxivId": "2310.08975", | |
| "title": "ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models" | |
| }, | |
| "2404.07103": { | |
| "arxivId": "2404.07103", | |
| "title": "Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs" | |
| }, | |
| "2305.18742": { | |
| "arxivId": "2305.18742", | |
| "title": "Graph Reasoning for Question Answering with Triplet Retrieval" | |
| }, | |
| "2405.04819": { | |
| "arxivId": "2405.04819", | |
| "title": "DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature" | |
| }, | |
| "2401.00426": { | |
| "arxivId": "2401.00426", | |
| "title": "keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM" | |
| }, | |
| "2311.03758": { | |
| "arxivId": "2311.03758", | |
| "title": "Large Language Model based Long-tail Query Rewriting in Taobao Search" | |
| }, | |
| "2404.17723": { | |
| "arxivId": "2404.17723", | |
| "title": "Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering" | |
| }, | |
| "2308.10173": { | |
| "arxivId": "2308.10173", | |
| "title": "FoodGPT: A Large Language Model in Food Testing Domain with Incremental Pre-training and Knowledge Graph Prompt" | |
| }, | |
| "2305.12416": { | |
| "arxivId": "2305.12416", | |
| "title": "Direct Fact Retrieval from Knowledge Graphs without Entity Linking" | |
| }, | |
| "2205.01841": { | |
| "arxivId": "2205.01841", | |
| "title": "Great Truths are Always Simple: A Rather Simple Knowledge Encoder for Enhancing the Commonsense Reasoning Capacity of Pre-Trained Models" | |
| }, | |
| "2403.05881": { | |
| "arxivId": "2403.05881", | |
| "title": "KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques" | |
| }, | |
| "2401.15569": { | |
| "arxivId": "2401.15569", | |
| "title": "Efficient Tuning and Inference for Large Language Models on Textual Graphs" | |
| }, | |
| "2312.15883": { | |
| "arxivId": "2312.15883", | |
| "title": "HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses" | |
| }, | |
| "2308.14436": { | |
| "arxivId": "2308.14436", | |
| "title": "Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA" | |
| }, | |
| "2303.12320": { | |
| "arxivId": "2303.12320", | |
| "title": "GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering" | |
| }, | |
| "2405.14831": { | |
| "arxivId": "2405.14831", | |
| "title": "HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models" | |
| }, | |
| "2211.10991": { | |
| "arxivId": "2211.10991", | |
| "title": "Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval" | |
| }, | |
| "2210.13650": { | |
| "arxivId": "2210.13650", | |
| "title": "ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs" | |
| }, | |
| "2404.00492": { | |
| "arxivId": "2404.00492", | |
| "title": "Multi-hop Question Answering under Temporal Knowledge Editing" | |
| }, | |
| "1606.05250": { | |
| "arxivId": "1606.05250", | |
| "title": "SQuAD: 100,000+ Questions for Machine Comprehension of Text" | |
| }, | |
| "2105.00691": { | |
| "arxivId": "2105.00691", | |
| "title": "Hybrid Intelligence" | |
| }, | |
| "2309.07930": { | |
| "arxivId": "2309.07930", | |
| "title": "Generative AI" | |
| }, | |
| "2201.11227": { | |
| "arxivId": "2201.11227", | |
| "title": "Synchromesh: Reliable code generation from pre-trained language models" | |
| }, | |
| "1808.10025": { | |
| "arxivId": "1808.10025", | |
| "title": "Retrieval-Based Neural Code Generation" | |
| }, | |
| "2401.05856": { | |
| "arxivId": "2401.05856", | |
| "title": "Seven Failure Points When Engineering a Retrieval Augmented Generation System" | |
| }, | |
| "2108.13934": { | |
| "arxivId": "2108.13934", | |
| "title": "Robust Retrieval Augmented Generation for Zero-shot Slot Filling" | |
| }, | |
| "1310.4546": { | |
| "arxivId": "1310.4546", | |
| "title": "Distributed Representations of Words and Phrases and their Compositionality" | |
| }, | |
| "1301.3781": { | |
| "arxivId": "1301.3781", | |
| "title": "Efficient Estimation of Word Representations in Vector Space" | |
| }, | |
| "1901.02860": { | |
| "arxivId": "1901.02860", | |
| "title": "Transformer-XL: Attentive Language Models beyond a Fixed-Length Context" | |
| }, | |
| "2004.05150": { | |
| "arxivId": "2004.05150", | |
| "title": "Longformer: The Long-Document Transformer" | |
| }, | |
| "2107.13586": { | |
| "arxivId": "2107.13586", | |
| "title": "Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing" | |
| }, | |
| "2109.01652": { | |
| "arxivId": "2109.01652", | |
| "title": "Finetuned Language Models Are Zero-Shot Learners" | |
| }, | |
| "2110.08207": { | |
| "arxivId": "2110.08207", | |
| "title": "Multitask Prompted Training Enables Zero-Shot Task Generalization" | |
| }, | |
| "2205.14135": { | |
| "arxivId": "2205.14135", | |
| "title": "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" | |
| }, | |
| "2003.08271": { | |
| "arxivId": "2003.08271", | |
| "title": "Pre-trained models for natural language processing: A survey" | |
| }, | |
| "2111.00396": { | |
| "arxivId": "2111.00396", | |
| "title": "Efficiently Modeling Long Sequences with Structured State Spaces" | |
| }, | |
| "2205.10625": { | |
| "arxivId": "2205.10625", | |
| "title": "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models" | |
| }, | |
| "2112.00861": { | |
| "arxivId": "2112.00861", | |
| "title": "A General Language Assistant as a Laboratory for Alignment" | |
| }, | |
| "2112.00114": { | |
| "arxivId": "2112.00114", | |
| "title": "Show Your Work: Scratchpads for Intermediate Computation with Language Models" | |
| }, | |
| "1808.08949": { | |
| "arxivId": "1808.08949", | |
| "title": "Dissecting Contextual Word Embeddings: Architecture and Representation" | |
| }, | |
| "2104.05240": { | |
| "arxivId": "2104.05240", | |
| "title": "Factual Probing Is [MASK]: Learning vs. Learning to Recall" | |
| }, | |
| "2306.15595": { | |
| "arxivId": "2306.15595", | |
| "title": "Extending Context Window of Large Language Models via Positional Interpolation" | |
| }, | |
| "2309.01219": { | |
| "arxivId": "2309.01219", | |
| "title": "Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models" | |
| }, | |
| "2208.04933": { | |
| "arxivId": "2208.04933", | |
| "title": "Simplified State Space Layers for Sequence Modeling" | |
| }, | |
| "2110.07178": { | |
| "arxivId": "2110.07178", | |
| "title": "Symbolic Knowledge Distillation: from General Language Models to Commonsense Models" | |
| }, | |
| "2212.14052": { | |
| "arxivId": "2212.14052", | |
| "title": "Hungry Hungry Hippos: Towards Language Modeling with State Space Models" | |
| }, | |
| "2302.10866": { | |
| "arxivId": "2302.10866", | |
| "title": "Hyena Hierarchy: Towards Larger Convolutional Language Models" | |
| }, | |
| "2304.08467": { | |
| "arxivId": "2304.08467", | |
| "title": "Learning to Compress Prompts with Gist Tokens" | |
| }, | |
| "2309.01431": { | |
| "arxivId": "2309.01431", | |
| "title": "Benchmarking Large Language Models in Retrieval-Augmented Generation" | |
| }, | |
| "2110.07814": { | |
| "arxivId": "2110.07814", | |
| "title": "Meta-learning via Language Model In-context Tuning" | |
| }, | |
| "2305.14788": { | |
| "arxivId": "2305.14788", | |
| "title": "Adapting Language Models to Compress Contexts" | |
| }, | |
| "2303.15647": { | |
| "arxivId": "2303.15647", | |
| "title": "Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning" | |
| }, | |
| "2310.06839": { | |
| "arxivId": "2310.06839", | |
| "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression" | |
| }, | |
| "2309.12307": { | |
| "arxivId": "2309.12307", | |
| "title": "LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models" | |
| }, | |
| "2207.06881": { | |
| "arxivId": "2207.06881", | |
| "title": "Recurrent Memory Transformer" | |
| }, | |
| "2310.05736": { | |
| "arxivId": "2310.05736", | |
| "title": "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models" | |
| }, | |
| "2305.13304": { | |
| "arxivId": "2305.13304", | |
| "title": "RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text" | |
| }, | |
| "2307.06945": { | |
| "arxivId": "2307.06945", | |
| "title": "In-context Autoencoder for Context Compression in a Large Language Model" | |
| }, | |
| "2210.03162": { | |
| "arxivId": "2210.03162", | |
| "title": "Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models" | |
| }, | |
| "2403.12968": { | |
| "arxivId": "2403.12968", | |
| "title": "LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression" | |
| }, | |
| "2209.15189": { | |
| "arxivId": "2209.15189", | |
| "title": "Learning by Distilling Context" | |
| }, | |
| "2311.12351": { | |
| "arxivId": "2311.12351", | |
| "title": "Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey" | |
| }, | |
| "2312.09571": { | |
| "arxivId": "2312.09571", | |
| "title": "Extending Context Window of Large Language Models via Semantic Compression" | |
| }, | |
| "2103.00020": { | |
| "arxivId": "2103.00020", | |
| "title": "Learning Transferable Visual Models From Natural Language Supervision" | |
| }, | |
| "2006.11239": { | |
| "arxivId": "2006.11239", | |
| "title": "Denoising Diffusion Probabilistic Models" | |
| }, | |
| "2112.10752": { | |
| "arxivId": "2112.10752", | |
| "title": "High-Resolution Image Synthesis with Latent Diffusion Models" | |
| }, | |
| "2302.13971": { | |
| "arxivId": "2302.13971", | |
| "title": "LLaMA: Open and Efficient Foundation Language Models" | |
| }, | |
| "2106.09685": { | |
| "arxivId": "2106.09685", | |
| "title": "LoRA: Low-Rank Adaptation of Large Language Models" | |
| }, | |
| "2201.11903": { | |
| "arxivId": "2201.11903", | |
| "title": "Chain of Thought Prompting Elicits Reasoning in Large Language Models" | |
| }, | |
| "2105.05233": { | |
| "arxivId": "2105.05233", | |
| "title": "Diffusion Models Beat GANs on Image Synthesis" | |
| }, | |
| "2204.06125": { | |
| "arxivId": "2204.06125", | |
| "title": "Hierarchical Text-Conditional Image Generation with CLIP Latents" | |
| }, | |
| "1503.03585": { | |
| "arxivId": "1503.03585", | |
| "title": "Deep Unsupervised Learning using Nonequilibrium Thermodynamics" | |
| }, | |
| "2010.02502": { | |
| "arxivId": "2010.02502", | |
| "title": "Denoising Diffusion Implicit Models" | |
| }, | |
| "2011.13456": { | |
| "arxivId": "2011.13456", | |
| "title": "Score-Based Generative Modeling through Stochastic Differential Equations" | |
| }, | |
| "2102.12092": { | |
| "arxivId": "2102.12092", | |
| "title": "Zero-Shot Text-to-Image Generation" | |
| }, | |
| "2001.08361": { | |
| "arxivId": "2001.08361", | |
| "title": "Scaling Laws for Neural Language Models" | |
| }, | |
| "1907.05600": { | |
| "arxivId": "1907.05600", | |
| "title": "Generative Modeling by Estimating Gradients of the Data Distribution" | |
| }, | |
| "2102.09672": { | |
| "arxivId": "2102.09672", | |
| "title": "Improved Denoising Diffusion Probabilistic Models" | |
| }, | |
| "1609.09430": { | |
| "arxivId": "1609.09430", | |
| "title": "CNN architectures for large-scale audio classification" | |
| }, | |
| "2002.08155": { | |
| "arxivId": "2002.08155", | |
| "title": "CodeBERT: A Pre-Trained Model for Programming and Natural Languages" | |
| }, | |
| "2101.03961": { | |
| "arxivId": "2101.03961", | |
| "title": "Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity" | |
| }, | |
| "2012.07805": { | |
| "arxivId": "2012.07805", | |
| "title": "Extracting Training Data from Large Language Models" | |
| }, | |
| "2308.12950": { | |
| "arxivId": "2308.12950", | |
| "title": "Code Llama: Open Foundation Models for Code" | |
| }, | |
| "2109.00859": { | |
| "arxivId": "2109.00859", | |
| "title": "CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation" | |
| }, | |
| "2210.02303": { | |
| "arxivId": "2210.02303", | |
| "title": "Imagen Video: High Definition Video Generation with Diffusion Models" | |
| }, | |
| "1901.04085": { | |
| "arxivId": "1901.04085", | |
| "title": "Passage Re-ranking with BERT" | |
| }, | |
| "2106.15282": { | |
| "arxivId": "2106.15282", | |
| "title": "Cascaded Diffusion Models for High Fidelity Image Generation" | |
| }, | |
| "2009.06732": { | |
| "arxivId": "2009.06732", | |
| "title": "Efficient Transformers: A Survey" | |
| }, | |
| "2006.09011": { | |
| "arxivId": "2006.09011", | |
| "title": "Improved Techniques for Training Score-Based Generative Models" | |
| }, | |
| "2009.08366": { | |
| "arxivId": "2009.08366", | |
| "title": "GraphCodeBERT: Pre-training Code Representations with Data Flow" | |
| }, | |
| "2102.04664": { | |
| "arxivId": "2102.04664", | |
| "title": "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" | |
| }, | |
| "2209.00796": { | |
| "arxivId": "2209.00796", | |
| "title": "Diffusion Models: A Comprehensive Survey of Methods and Applications" | |
| }, | |
| "2104.00650": { | |
| "arxivId": "2104.00650", | |
| "title": "Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval" | |
| }, | |
| "2001.06937": { | |
| "arxivId": "2001.06937", | |
| "title": "A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications" | |
| }, | |
| "2107.03006": { | |
| "arxivId": "2107.03006", | |
| "title": "Structured Denoising Diffusion Models in Discrete State-Spaces" | |
| }, | |
| "2103.06333": { | |
| "arxivId": "2103.06333", | |
| "title": "Unified Pre-training for Program Understanding and Generation" | |
| }, | |
| "2205.14217": { | |
| "arxivId": "2205.14217", | |
| "title": "Diffusion-LM Improves Controllable Text Generation" | |
| }, | |
| "2303.01469": { | |
| "arxivId": "2303.01469", | |
| "title": "Consistency Models" | |
| }, | |
| "2305.16213": { | |
| "arxivId": "2305.16213", | |
| "title": "ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation" | |
| }, | |
| "2010.08191": { | |
| "arxivId": "2010.08191", | |
| "title": "RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering" | |
| }, | |
| "2101.09258": { | |
| "arxivId": "2101.09258", | |
| "title": "Maximum Likelihood Training of Score-Based Diffusion Models" | |
| }, | |
| "2203.17003": { | |
| "arxivId": "2203.17003", | |
| "title": "Equivariant Diffusion for Molecule Generation in 3D" | |
| }, | |
| "2104.14951": { | |
| "arxivId": "2104.14951", | |
| "title": "SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models" | |
| }, | |
| "2203.02923": { | |
| "arxivId": "2203.02923", | |
| "title": "GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation" | |
| }, | |
| "2209.03003": { | |
| "arxivId": "2209.03003", | |
| "title": "Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow" | |
| }, | |
| "2208.15001": { | |
| "arxivId": "2208.15001", | |
| "title": "MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model" | |
| }, | |
| "2212.10511": { | |
| "arxivId": "2212.10511", | |
| "title": "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories" | |
| }, | |
| "2109.05014": { | |
| "arxivId": "2109.05014", | |
| "title": "An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA" | |
| }, | |
| "2211.06687": { | |
| "arxivId": "2211.06687", | |
| "title": "Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation" | |
| }, | |
| "2305.07922": { | |
| "arxivId": "2305.07922", | |
| "title": "CodeT5+: Open Code Large Language Models for Code Understanding and Generation" | |
| }, | |
| "2012.00955": { | |
| "arxivId": "2012.00955", | |
| "title": "How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering" | |
| }, | |
| "2309.07597": { | |
| "arxivId": "2309.07597", | |
| "title": "C-Pack: Packaged Resources To Advance General Chinese Embedding" | |
| }, | |
| "2302.00923": { | |
| "arxivId": "2302.00923", | |
| "title": "Multimodal Chain-of-Thought Reasoning in Language Models" | |
| }, | |
| "2010.00710": { | |
| "arxivId": "2010.00710", | |
| "title": "Nearest Neighbor Machine Translation" | |
| }, | |
| "2211.08411": { | |
| "arxivId": "2211.08411", | |
| "title": "Large Language Models Struggle to Learn Long-Tail Knowledge" | |
| }, | |
| "1809.06181": { | |
| "arxivId": "1809.06181", | |
| "title": "Dual Encoding for Zero-Example Video Retrieval" | |
| }, | |
| "2104.08051": { | |
| "arxivId": "2104.08051", | |
| "title": "Optimizing Dense Retrieval Model Training with Hard Negatives" | |
| }, | |
| "2210.08933": { | |
| "arxivId": "2210.08933", | |
| "title": "DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models" | |
| }, | |
| "2205.11495": { | |
| "arxivId": "2205.11495", | |
| "title": "Flexible Diffusion Modeling of Long Videos" | |
| }, | |
| "2104.08253": { | |
| "arxivId": "2104.08253", | |
| "title": "Condenser: a Pre-training Architecture for Dense Retrieval" | |
| }, | |
| "2301.12661": { | |
| "arxivId": "2301.12661", | |
| "title": "Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models" | |
| }, | |
| "2208.04202": { | |
| "arxivId": "2208.04202", | |
| "title": "Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning" | |
| }, | |
| "2208.03188": { | |
| "arxivId": "2208.03188", | |
| "title": "BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage" | |
| }, | |
| "2206.01729": { | |
| "arxivId": "2206.01729", | |
| "title": "Torsional Diffusion for Molecular Conformer Generation" | |
| }, | |
| "2203.09481": { | |
| "arxivId": "2203.09481", | |
| "title": "Diffusion Probabilistic Modeling for Video Generation" | |
| }, | |
| "1904.11574": { | |
| "arxivId": "1904.11574", | |
| "title": "TVQA+: Spatio-Temporal Grounding for Video Question Answering" | |
| }, | |
| "2012.12627": { | |
| "arxivId": "2012.12627", | |
| "title": "Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing" | |
| }, | |
| "2102.10407": { | |
| "arxivId": "2102.10407", | |
| "title": "VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning" | |
| }, | |
| "1812.01194": { | |
| "arxivId": "1812.01194", | |
| "title": "A Retrieve-and-Edit Framework for Predicting Structured Outputs" | |
| }, | |
| "2009.12677": { | |
| "arxivId": "2009.12677", | |
| "title": "KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning" | |
| }, | |
| "2210.07128": { | |
| "arxivId": "2210.07128", | |
| "title": "Language Models of Code are Few-Shot Commonsense Learners" | |
| }, | |
| "2203.13474": { | |
| "arxivId": "2203.13474", | |
| "title": "A Conversational Paradigm for Program Synthesis" | |
| }, | |
| "2205.15019": { | |
| "arxivId": "2205.15019", | |
| "title": "Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models" | |
| }, | |
| "2104.08762": { | |
| "arxivId": "2104.08762", | |
| "title": "Case-based Reasoning for Natural Language Queries over Knowledge Bases" | |
| }, | |
| "2006.05405": { | |
| "arxivId": "2006.05405", | |
| "title": "Retrieval-Augmented Generation for Code Summarization via Hybrid GNN" | |
| }, | |
| "2303.12570": { | |
| "arxivId": "2303.12570", | |
| "title": "RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation" | |
| }, | |
| "2306.15626": { | |
| "arxivId": "2306.15626", | |
| "title": "LeanDojo: Theorem Proving with Retrieval-Augmented Language Models" | |
| }, | |
| "2104.12836": { | |
| "arxivId": "2104.12836", | |
| "title": "Multimodal Contrastive Training for Visual Representation Learning" | |
| }, | |
| "2109.05070": { | |
| "arxivId": "2109.05070", | |
| "title": "Instance-Conditioned GAN" | |
| }, | |
| "2402.03216": { | |
| "arxivId": "2402.03216", | |
| "title": "BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation" | |
| }, | |
| "2206.02743": { | |
| "arxivId": "2206.02743", | |
| "title": "A Neural Corpus Indexer for Document Retrieval" | |
| }, | |
| "2203.07722": { | |
| "arxivId": "2203.07722", | |
| "title": "ReACC: A Retrieval-Augmented Code Completion Framework" | |
| }, | |
| "2204.11824": { | |
| "arxivId": "2204.11824", | |
| "title": "Retrieval-Augmented Diffusion Models" | |
| }, | |
| "1910.10419": { | |
| "arxivId": "1910.10419", | |
| "title": "Retrieve and Refine: Exemplar-Based Neural Comment Generation" | |
| }, | |
| "2205.10747": { | |
| "arxivId": "2205.10747", | |
| "title": "Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners" | |
| }, | |
| "2302.05965": { | |
| "arxivId": "2302.05965", | |
| "title": "RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL" | |
| }, | |
| "2110.03611": { | |
| "arxivId": "2110.03611", | |
| "title": "Adversarial Retriever-Ranker for dense text retrieval" | |
| }, | |
| "2304.09667": { | |
| "arxivId": "2304.09667", | |
| "title": "GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information" | |
| }, | |
| "2305.01625": { | |
| "arxivId": "2305.01625", | |
| "title": "Unlimiformer: Long-Range Transformers with Unlimited Length Input" | |
| }, | |
| "2208.11640": { | |
| "arxivId": "2208.11640", | |
| "title": "Repair Is Nearly Generation: Multilingual Program Repair with LLMs" | |
| }, | |
| "2303.07263": { | |
| "arxivId": "2303.07263", | |
| "title": "InferFix: End-to-End Program Repair with LLMs" | |
| }, | |
| "2309.15217": { | |
| "arxivId": "2309.15217", | |
| "title": "RAGAs: Automated Evaluation of Retrieval Augmented Generation" | |
| }, | |
| "2302.12246": { | |
| "arxivId": "2302.12246", | |
| "title": "Active Prompting with Chain-of-Thought for Large Language Models" | |
| }, | |
| "2105.11269": { | |
| "arxivId": "2105.11269", | |
| "title": "Neural Machine Translation with Monolingual Translation Memory" | |
| }, | |
| "2307.11019": { | |
| "arxivId": "2307.11019", | |
| "title": "Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation" | |
| }, | |
| "2402.04333": { | |
| "arxivId": "2402.04333", | |
| "title": "LESS: Selecting Influential Data for Targeted Instruction Tuning" | |
| }, | |
| "1809.05296": { | |
| "arxivId": "1809.05296", | |
| "title": "Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory" | |
| }, | |
| "2110.04330": { | |
| "arxivId": "2110.04330", | |
| "title": "KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering" | |
| }, | |
| "2012.14610": { | |
| "arxivId": "2012.14610", | |
| "title": "UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering" | |
| }, | |
| "2004.12744": { | |
| "arxivId": "2004.12744", | |
| "title": "Augmenting Transformers with KNN-Based Composite Memory for Dialog" | |
| }, | |
| "2308.16137": { | |
| "arxivId": "2308.16137", | |
| "title": "LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models" | |
| }, | |
| "2303.07678": { | |
| "arxivId": "2303.07678", | |
| "title": "Query2doc: Query Expansion with Large Language Models" | |
| }, | |
| "2107.11976": { | |
| "arxivId": "2107.11976", | |
| "title": "One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval" | |
| }, | |
| "2207.13038": { | |
| "arxivId": "2207.13038", | |
| "title": "Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models" | |
| }, | |
| "2101.00133": { | |
| "arxivId": "2101.00133", | |
| "title": "NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned" | |
| }, | |
| "2401.18059": { | |
| "arxivId": "2401.18059", | |
| "title": "RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval" | |
| }, | |
| "2302.06144": { | |
| "arxivId": "2302.06144", | |
| "title": "SkCoder: A Sketch-based Approach for Automatic Code Generation" | |
| }, | |
| "2308.13775": { | |
| "arxivId": "2308.13775", | |
| "title": "EditSum: A Retrieve-and-Edit Framework for Source Code Summarization" | |
| }, | |
| "2403.14403": { | |
| "arxivId": "2403.14403", | |
| "title": "Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity" | |
| }, | |
| "2210.12925": { | |
| "arxivId": "2210.12925", | |
| "title": "TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base" | |
| }, | |
| "2401.11708": { | |
| "arxivId": "2401.11708", | |
| "title": "Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs" | |
| }, | |
| "2304.06815": { | |
| "arxivId": "2304.06815", | |
| "title": "Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)" | |
| }, | |
| "2303.10868": { | |
| "arxivId": "2303.10868", | |
| "title": "Retrieving Multimodal Information for Augmented Generation: A Survey" | |
| }, | |
| "2303.06573": { | |
| "arxivId": "2303.06573", | |
| "title": "Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search" | |
| }, | |
| "2212.10007": { | |
| "arxivId": "2212.10007", | |
| "title": "CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context" | |
| }, | |
| "2210.00063": { | |
| "arxivId": "2210.00063", | |
| "title": "DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases" | |
| }, | |
| "2210.03809": { | |
| "arxivId": "2210.03809", | |
| "title": "Retrieval Augmented Visual Question Answering with Outside Knowledge" | |
| }, | |
| "2007.08513": { | |
| "arxivId": "2007.08513", | |
| "title": "RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval" | |
| }, | |
| "2205.12253": { | |
| "arxivId": "2205.12253", | |
| "title": "Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing" | |
| }, | |
| "2309.11325": { | |
| "arxivId": "2309.11325", | |
| "title": "DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services" | |
| }, | |
| "2212.01588": { | |
| "arxivId": "2212.01588", | |
| "title": "RHO ($\u03c1$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding" | |
| }, | |
| "2311.08377": { | |
| "arxivId": "2311.08377", | |
| "title": "Learning to Filter Context for Retrieval-Augmented Generation" | |
| }, | |
| "2110.06176": { | |
| "arxivId": "2110.06176", | |
| "title": "MENTION MEMORY : INCORPORATING TEXTUAL KNOWLEDGE INTO TRANSFORMERS THROUGH ENTITY MENTION ATTENTION" | |
| }, | |
| "2311.08252": { | |
| "arxivId": "2311.08252", | |
| "title": "REST: Retrieval-Based Speculative Decoding" | |
| }, | |
| "2309.05767": { | |
| "arxivId": "2309.05767", | |
| "title": "Natural Language Supervision For General-Purpose Audio Representations" | |
| }, | |
| "2012.07331": { | |
| "arxivId": "2012.07331", | |
| "title": "Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval" | |
| }, | |
| "2307.06940": { | |
| "arxivId": "2307.06940", | |
| "title": "Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation" | |
| }, | |
| "2401.15884": { | |
| "arxivId": "2401.15884", | |
| "title": "Corrective Retrieval Augmented Generation" | |
| }, | |
| "2203.10299": { | |
| "arxivId": "2203.10299", | |
| "title": "Neural Machine Translation with Phrase-Level Universal Visual Representations" | |
| }, | |
| "2310.07554": { | |
| "arxivId": "2310.07554", | |
| "title": "Retrieve Anything To Augment Large Language Models" | |
| }, | |
| "2309.06057": { | |
| "arxivId": "2309.06057", | |
| "title": "RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair" | |
| }, | |
| "2204.11677": { | |
| "arxivId": "2204.11677", | |
| "title": "Conversational Question Answering on Heterogeneous Sources" | |
| }, | |
| "2402.16347": { | |
| "arxivId": "2402.16347", | |
| "title": "CodeS: Towards Building Open-source Language Models for Text-to-SQL" | |
| }, | |
| "2207.03637": { | |
| "arxivId": "2207.03637", | |
| "title": "OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering" | |
| }, | |
| "2401.15391": { | |
| "arxivId": "2401.15391", | |
| "title": "MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries" | |
| }, | |
| "2211.07067": { | |
| "arxivId": "2211.07067", | |
| "title": "Retrieval-Augmented Generative Question Answering for Event Argument Extraction" | |
| }, | |
| "2108.02866": { | |
| "arxivId": "2108.02866", | |
| "title": "Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering" | |
| }, | |
| "2401.07339": { | |
| "arxivId": "2401.07339", | |
| "title": "CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges" | |
| }, | |
| "2203.02700": { | |
| "arxivId": "2203.02700", | |
| "title": "RACE: Retrieval-augmented Commit Message Generation" | |
| }, | |
| "2106.06471": { | |
| "arxivId": "2106.06471", | |
| "title": "Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation" | |
| }, | |
| "2311.16543": { | |
| "arxivId": "2311.16543", | |
| "title": "RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models" | |
| }, | |
| "2305.03653": { | |
| "arxivId": "2305.03653", | |
| "title": "Query Expansion by Prompting Large Language Models" | |
| }, | |
| "2208.11126": { | |
| "arxivId": "2208.11126", | |
| "title": "Retrieval-based Controllable Molecule Generation" | |
| }, | |
| "2105.13073": { | |
| "arxivId": "2105.13073", | |
| "title": "Maria: A Visual Experience Powered Conversational Agent" | |
| }, | |
| "2307.07164": { | |
| "arxivId": "2307.07164", | |
| "title": "Learning to Retrieve In-Context Examples for Large Language Models" | |
| }, | |
| "2303.00807": { | |
| "arxivId": "2303.00807", | |
| "title": "UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers" | |
| }, | |
| "2403.05313": { | |
| "arxivId": "2403.05313", | |
| "title": "RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation" | |
| }, | |
| "2402.10790": { | |
| "arxivId": "2402.10790", | |
| "title": "In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss" | |
| }, | |
| "2402.07630": { | |
| "arxivId": "2402.07630", | |
| "title": "G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering" | |
| }, | |
| "2404.00610": { | |
| "arxivId": "2404.00610", | |
| "title": "RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation" | |
| }, | |
| "2402.10828": { | |
| "arxivId": "2402.10828", | |
| "title": "RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model" | |
| }, | |
| "2210.02933": { | |
| "arxivId": "2210.02933", | |
| "title": "Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering" | |
| }, | |
| "2306.10998": { | |
| "arxivId": "2306.10998", | |
| "title": "RepoFusion: Training Code Models to Understand Your Repository" | |
| }, | |
| "2311.06318": { | |
| "arxivId": "2311.06318", | |
| "title": "Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion" | |
| }, | |
| "2311.02962": { | |
| "arxivId": "2311.02962", | |
| "title": "Retrieval-Augmented Code Generation for Universal Information Extraction" | |
| }, | |
| "2310.03184": { | |
| "arxivId": "2310.03184", | |
| "title": "Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference" | |
| }, | |
| "2302.08268": { | |
| "arxivId": "2302.08268", | |
| "title": "Retrieval-augmented Image Captioning" | |
| }, | |
| "2303.17780": { | |
| "arxivId": "2303.17780", | |
| "title": "AceCoder: Utilizing Existing Code to Enhance Code Generation" | |
| }, | |
| "2211.08380": { | |
| "arxivId": "2211.08380", | |
| "title": "Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering" | |
| }, | |
| "2206.13325": { | |
| "arxivId": "2206.13325", | |
| "title": "BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT" | |
| }, | |
| "2104.07921": { | |
| "arxivId": "2104.07921", | |
| "title": "VGNMN: Video-grounded Neural Module Networks for Video-Grounded Dialogue Systems" | |
| }, | |
| "2402.03181": { | |
| "arxivId": "2402.03181", | |
| "title": "C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models" | |
| }, | |
| "2401.02015": { | |
| "arxivId": "2401.02015", | |
| "title": "Improving Diffusion-Based Image Synthesis with Context Prediction" | |
| }, | |
| "2310.14696": { | |
| "arxivId": "2310.14696", | |
| "title": "Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models" | |
| }, | |
| "2403.10446": { | |
| "arxivId": "2403.10446", | |
| "title": "Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases" | |
| }, | |
| "2402.11782": { | |
| "arxivId": "2402.11782", | |
| "title": "What Evidence Do Language Models Find Convincing?" | |
| }, | |
| "2306.06156": { | |
| "arxivId": "2306.06156", | |
| "title": "PoET: A generative model of protein families as sequences-of-sequences" | |
| }, | |
| "2310.15657": { | |
| "arxivId": "2310.15657", | |
| "title": "Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model" | |
| }, | |
| "2306.11732": { | |
| "arxivId": "2306.11732", | |
| "title": "Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models" | |
| }, | |
| "2305.04032": { | |
| "arxivId": "2305.04032", | |
| "title": "ToolCoder: Teach Code Generation Models to use API search tools" | |
| }, | |
| "2306.14722": { | |
| "arxivId": "2306.14722", | |
| "title": "FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering" | |
| }, | |
| "2302.05578": { | |
| "arxivId": "2302.05578", | |
| "title": "Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models" | |
| }, | |
| "2212.08632": { | |
| "arxivId": "2212.08632", | |
| "title": "Enhancing Multi-modal Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation" | |
| }, | |
| "2202.13972": { | |
| "arxivId": "2202.13972", | |
| "title": "The impact of lexical and grammatical processing on generating code from natural language" | |
| }, | |
| "2311.13534": { | |
| "arxivId": "2311.13534", | |
| "title": "LM-Cocktail: Resilient Tuning of Language Models via Model Merging" | |
| }, | |
| "2212.09651": { | |
| "arxivId": "2212.09651", | |
| "title": "Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages" | |
| }, | |
| "2205.10471": { | |
| "arxivId": "2205.10471", | |
| "title": "Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training" | |
| }, | |
| "2203.16714": { | |
| "arxivId": "2203.16714", | |
| "title": "End-to-End Table Question Answering via Retrieval-Augmented Generation" | |
| }, | |
| "2401.01701": { | |
| "arxivId": "2401.01701", | |
| "title": "De-Hallucinator: Iterative Grounding for LLM-Based Code Completion" | |
| }, | |
| "2404.07220": { | |
| "arxivId": "2404.07220", | |
| "title": "Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers" | |
| }, | |
| "2401.17043": { | |
| "arxivId": "2401.17043", | |
| "title": "CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models" | |
| }, | |
| "2310.20158": { | |
| "arxivId": "2310.20158", | |
| "title": "GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval" | |
| }, | |
| "2210.12338": { | |
| "arxivId": "2210.12338", | |
| "title": "Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge" | |
| }, | |
| "2209.02071": { | |
| "arxivId": "2209.02071", | |
| "title": "CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval" | |
| }, | |
| "2401.07883": { | |
| "arxivId": "2401.07883", | |
| "title": "The Chronicles of RAG: The Retriever, the Chunk and the Generator" | |
| }, | |
| "2310.06302": { | |
| "arxivId": "2310.06302", | |
| "title": "Selective Demonstrations for Cross-domain Text-to-SQL" | |
| }, | |
| "2308.09313": { | |
| "arxivId": "2308.09313", | |
| "title": "Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases" | |
| }, | |
| "2305.18170": { | |
| "arxivId": "2305.18170", | |
| "title": "Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning" | |
| }, | |
| "2208.07022": { | |
| "arxivId": "2208.07022", | |
| "title": "Memory-Driven Text-to-Image Generation" | |
| }, | |
| "2402.12908": { | |
| "arxivId": "2402.12908", | |
| "title": "RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models" | |
| }, | |
| "2402.16063": { | |
| "arxivId": "2402.16063", | |
| "title": "Citation-Enhanced Generation for LLM-based Chatbots" | |
| }, | |
| "2402.12317": { | |
| "arxivId": "2402.12317", | |
| "title": "ARKS: Active Retrieval in Knowledge Soup for Code Generation" | |
| }, | |
| "2401.13256": { | |
| "arxivId": "2401.13256", | |
| "title": "UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems" | |
| }, | |
| "2309.07372": { | |
| "arxivId": "2309.07372", | |
| "title": "Training Audio Captioning Models without Audio" | |
| }, | |
| "2401.06800": { | |
| "arxivId": "2401.06800", | |
| "title": "Reinforcement Learning for Optimizing RAG for Domain Chatbots" | |
| }, | |
| "2309.09836": { | |
| "arxivId": "2309.09836", | |
| "title": "Recap: Retrieval-Augmented Audio Captioning" | |
| }, | |
| "1409.3215": { | |
| "arxivId": "1409.3215", | |
| "title": "Sequence to Sequence Learning with Neural Networks" | |
| }, | |
| "1506.02626": { | |
| "arxivId": "1506.02626", | |
| "title": "Learning both Weights and Connections for Efficient Neural Network" | |
| }, | |
| "2403.10131": { | |
| "arxivId": "2403.10131", | |
| "title": "RAFT: Adapting Language Model to Domain Specific RAG" | |
| }, | |
| "2211.12561": { | |
| "arxivId": "2211.12561", | |
| "title": "Retrieval-Augmented Multimodal Language Modeling" | |
| }, | |
| "2312.15503": { | |
| "arxivId": "2312.15503", | |
| "title": "Making Large Language Models A Better Foundation For Dense Retrieval" | |
| }, | |
| "2308.14263": { | |
| "arxivId": "2308.14263", | |
| "title": "Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions" | |
| }, | |
| "1902.00751": { | |
| "arxivId": "1902.00751", | |
| "title": "Parameter-Efficient Transfer Learning for NLP" | |
| }, | |
| "2201.12086": { | |
| "arxivId": "2201.12086", | |
| "title": "BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation" | |
| }, | |
| "1611.09268": { | |
| "arxivId": "1611.09268", | |
| "title": "MS MARCO: A Human Generated MAchine Reading COmprehension Dataset" | |
| }, | |
| "2203.11171": { | |
| "arxivId": "2203.11171", | |
| "title": "Self-Consistency Improves Chain of Thought Reasoning in Language Models" | |
| }, | |
| "2305.10601": { | |
| "arxivId": "2305.10601", | |
| "title": "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" | |
| }, | |
| "1903.00161": { | |
| "arxivId": "1903.00161", | |
| "title": "DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs" | |
| }, | |
| "2110.04366": { | |
| "arxivId": "2110.04366", | |
| "title": "Towards a Unified View of Parameter-Efficient Transfer Learning" | |
| }, | |
| "2304.12244": { | |
| "arxivId": "2304.12244", | |
| "title": "WizardLM: Empowering Large Language Models to Follow Complex Instructions" | |
| }, | |
| "2303.11366": { | |
| "arxivId": "2303.11366", | |
| "title": "Reflexion: language agents with verbal reinforcement learning" | |
| }, | |
| "2211.01910": { | |
| "arxivId": "2211.01910", | |
| "title": "Large Language Models Are Human-Level Prompt Engineers" | |
| }, | |
| "2308.11432": { | |
| "arxivId": "2308.11432", | |
| "title": "A Survey on Large Language Model based Autonomous Agents" | |
| }, | |
| "2101.02235": { | |
| "arxivId": "2101.02235", | |
| "title": "Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies" | |
| }, | |
| "2301.13688": { | |
| "arxivId": "2301.13688", | |
| "title": "The Flan Collection: Designing Data and Methods for Effective Instruction Tuning" | |
| }, | |
| "2304.07327": { | |
| "arxivId": "2304.07327", | |
| "title": "OpenAssistant Conversations - Democratizing Large Language Model Alignment" | |
| }, | |
| "2210.03350": { | |
| "arxivId": "2210.03350", | |
| "title": "Measuring and Narrowing the Compositionality Gap in Language Models" | |
| }, | |
| "2210.03493": { | |
| "arxivId": "2210.03493", | |
| "title": "Automatic Chain of Thought Prompting in Large Language Models" | |
| }, | |
| "2302.00093": { | |
| "arxivId": "2302.00093", | |
| "title": "Large Language Models Can Be Easily Distracted by Irrelevant Context" | |
| }, | |
| "2308.00352": { | |
| "arxivId": "2308.00352", | |
| "title": "MetaGPT: Meta Programming for Multi-Agent Collaborative Framework" | |
| }, | |
| "2005.00181": { | |
| "arxivId": "2005.00181", | |
| "title": "Sparse, Dense, and Attentional Representations for Text Retrieval" | |
| }, | |
| "2308.10792": { | |
| "arxivId": "2308.10792", | |
| "title": "Instruction Tuning for Large Language Models: A Survey" | |
| }, | |
| "2104.06967": { | |
| "arxivId": "2104.06967", | |
| "title": "Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling" | |
| }, | |
| "2303.08128": { | |
| "arxivId": "2303.08128", | |
| "title": "ViperGPT: Visual Inference via Python Execution for Reasoning" | |
| }, | |
| "2306.04751": { | |
| "arxivId": "2306.04751", | |
| "title": "How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources" | |
| }, | |
| "2203.14465": { | |
| "arxivId": "2203.14465", | |
| "title": "STaR: Bootstrapping Reasoning With Reasoning" | |
| }, | |
| "2205.12548": { | |
| "arxivId": "2205.12548", | |
| "title": "RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning" | |
| }, | |
| "2309.05653": { | |
| "arxivId": "2309.05653", | |
| "title": "MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning" | |
| }, | |
| "2106.04489": { | |
| "arxivId": "2106.04489", | |
| "title": "Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks" | |
| }, | |
| "2309.03409": { | |
| "arxivId": "2309.03409", | |
| "title": "Large Language Models as Optimizers" | |
| }, | |
| "2011.01060": { | |
| "arxivId": "2011.01060", | |
| "title": "Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps" | |
| }, | |
| "2212.12017": { | |
| "arxivId": "2212.12017", | |
| "title": "OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization" | |
| }, | |
| "2303.14070": { | |
| "arxivId": "2303.14070", | |
| "title": "ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge" | |
| }, | |
| "2308.00692": { | |
| "arxivId": "2308.00692", | |
| "title": "LISA: Reasoning Segmentation via Large Language Model" | |
| }, | |
| "2105.03011": { | |
| "arxivId": "2105.03011", | |
| "title": "A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers" | |
| }, | |
| "2311.16452": { | |
| "arxivId": "2311.16452", | |
| "title": "Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine" | |
| }, | |
| "2305.03495": { | |
| "arxivId": "2305.03495", | |
| "title": "Automatic Prompt Optimization with \"Gradient Descent\" and Beam Search" | |
| }, | |
| "2304.11015": { | |
| "arxivId": "2304.11015", | |
| "title": "DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction" | |
| }, | |
| "2309.12288": { | |
| "arxivId": "2309.12288", | |
| "title": "The Reversal Curse: LLMs trained on \"A is B\" fail to learn \"B is A\"" | |
| }, | |
| "2402.09353": { | |
| "arxivId": "2402.09353", | |
| "title": "DoRA: Weight-Decomposed Low-Rank Adaptation" | |
| }, | |
| "2108.00573": { | |
| "arxivId": "2108.00573", | |
| "title": "\u266b MuSiQue: Multihop Questions via Single-hop Question Composition" | |
| }, | |
| "2106.05707": { | |
| "arxivId": "2106.05707", | |
| "title": "FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information" | |
| }, | |
| "2305.07001": { | |
| "arxivId": "2305.07001", | |
| "title": "Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach" | |
| }, | |
| "2306.06031": { | |
| "arxivId": "2306.06031", | |
| "title": "FinGPT: Open-Source Financial Large Language Models" | |
| }, | |
| "2204.06092": { | |
| "arxivId": "2204.06092", | |
| "title": "ASQA: Factoid Questions Meet Long-Form Answers" | |
| }, | |
| "2403.14608": { | |
| "arxivId": "2403.14608", | |
| "title": "Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey" | |
| }, | |
| "2210.07558": { | |
| "arxivId": "2210.07558", | |
| "title": "DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation" | |
| }, | |
| "2112.08608": { | |
| "arxivId": "2112.08608", | |
| "title": "QuALITY: Question Answering with Long Input Texts, Yes!" | |
| }, | |
| "2302.12822": { | |
| "arxivId": "2302.12822", | |
| "title": "Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data" | |
| }, | |
| "2203.07281": { | |
| "arxivId": "2203.07281", | |
| "title": "GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models" | |
| }, | |
| "2308.11462": { | |
| "arxivId": "2308.11462", | |
| "title": "LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models" | |
| }, | |
| "2311.10537": { | |
| "arxivId": "2311.10537", | |
| "title": "MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning" | |
| }, | |
| "2301.11916": { | |
| "arxivId": "2301.11916", | |
| "title": "Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning" | |
| }, | |
| "2404.05961": { | |
| "arxivId": "2404.05961", | |
| "title": "LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders" | |
| }, | |
| "2108.08513": { | |
| "arxivId": "2108.08513", | |
| "title": "Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion" | |
| }, | |
| "2402.00157": { | |
| "arxivId": "2402.00157", | |
| "title": "Large Language Models for Mathematical Reasoning: Progresses and Challenges" | |
| }, | |
| "1610.10001": { | |
| "arxivId": "1610.10001", | |
| "title": "Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search" | |
| }, | |
| "2306.08640": { | |
| "arxivId": "2306.08640", | |
| "title": "AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn" | |
| }, | |
| "2302.07027": { | |
| "arxivId": "2302.07027", | |
| "title": "AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models" | |
| }, | |
| "2305.14283": { | |
| "arxivId": "2305.14283", | |
| "title": "Query Rewriting for Retrieval-Augmented Large Language Models" | |
| }, | |
| "2405.05904": { | |
| "arxivId": "2405.05904", | |
| "title": "Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?" | |
| }, | |
| "2310.02374": { | |
| "arxivId": "2310.02374", | |
| "title": "Conversational Health Agents: A Personalized LLM-Powered Agent Framework" | |
| }, | |
| "2404.11018": { | |
| "arxivId": "2404.11018", | |
| "title": "Many-Shot In-Context Learning" | |
| }, | |
| "2303.10512": { | |
| "arxivId": "2303.10512", | |
| "title": "AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning" | |
| }, | |
| "2303.02913": { | |
| "arxivId": "2303.02913", | |
| "title": "OpenICL: An Open-Source Framework for In-context Learning" | |
| }, | |
| "2304.04947": { | |
| "arxivId": "2304.04947", | |
| "title": "Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference" | |
| }, | |
| "2405.02957": { | |
| "arxivId": "2405.02957", | |
| "title": "Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents" | |
| }, | |
| "2211.11890": { | |
| "arxivId": "2211.11890", | |
| "title": "TEMPERA: Test-Time Prompting via Reinforcement Learning" | |
| }, | |
| "2310.07713": { | |
| "arxivId": "2310.07713", | |
| "title": "InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining" | |
| }, | |
| "2303.08119": { | |
| "arxivId": "2303.08119", | |
| "title": "How Many Demonstrations Do You Need for In-context Learning?" | |
| }, | |
| "2310.08184": { | |
| "arxivId": "2310.08184", | |
| "title": "Learn From Model Beyond Fine-Tuning: A Survey" | |
| }, | |
| "2304.14979": { | |
| "arxivId": "2304.14979", | |
| "title": "MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks" | |
| }, | |
| "2311.11696": { | |
| "arxivId": "2311.11696", | |
| "title": "Sparse Low-rank Adaptation of Pre-trained Language Models" | |
| }, | |
| "2305.09955": { | |
| "arxivId": "2305.09955", | |
| "title": "Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models" | |
| }, | |
| "2212.08286": { | |
| "arxivId": "2212.08286", | |
| "title": "ALERT: Adapt Language Models to Reasoning Tasks" | |
| }, | |
| "2401.08967": { | |
| "arxivId": "2401.08967", | |
| "title": "ReFT: Reasoning with Reinforced Fine-Tuning" | |
| }, | |
| "2310.05149": { | |
| "arxivId": "2310.05149", | |
| "title": "Retrieval-Generation Synergy Augmented Large Language Models" | |
| }, | |
| "2402.05403": { | |
| "arxivId": "2402.05403", | |
| "title": "In-Context Principle Learning from Mistakes" | |
| }, | |
| "2312.06648": { | |
| "arxivId": "2312.06648", | |
| "title": "Dense X Retrieval: What Retrieval Granularity Should We Use?" | |
| }, | |
| "2310.19698": { | |
| "arxivId": "2310.19698", | |
| "title": "When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations" | |
| }, | |
| "2404.14851": { | |
| "arxivId": "2404.14851", | |
| "title": "From Matching to Generation: A Survey on Generative Information Retrieval" | |
| }, | |
| "2310.05066": { | |
| "arxivId": "2310.05066", | |
| "title": "Guideline Learning for In-context Information Extraction" | |
| }, | |
| "2406.11903": { | |
| "arxivId": "2406.11903", | |
| "title": "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges" | |
| }, | |
| "2402.05131": { | |
| "arxivId": "2402.05131", | |
| "title": "Financial Report Chunking for Effective Retrieval Augmented Generation" | |
| } | |
| } |