Upload README.md

README.md

@@ -14,37 +14,6 @@ tags:
 - regression
 size_categories:
 - 100<n<1K
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: 'Ep #'
-    dtype: string
-  - name: Misery
-    dtype: string
-  - name: Score
-    dtype: int64
-  - name: VNTO
-    dtype: string
-  - name: Reward
-    dtype: int64
-  - name: Win
-    dtype: string
-  - name: Comments
-    dtype: string
-  - name: question_tag
-    dtype: string
-  - name: level
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 53815
-    num_examples: 516
-  download_size: 24234
-  dataset_size: 53815
 ---
 
 # Misery Index Dataset
@@ -53,6 +22,8 @@ dataset_info:
 
 The Misery Index Dataset comprises 516 textual descriptions of real-world or imagined scenarios, each annotated with a corresponding misery score on a continuous scale from 0 (no misery) to 100 (extreme misery). These misery ratings represent subjective estimates of emotional distress associated with each event.
 
+This dataset was created and analyzed in the research paper ["Leveraging Large Language Models for Predictive Analysis of Human Misery"](https://arxiv.org/pdf/2508.12669) by Bishanka Seal, Rahul Seetharaman, Aman Bansal, and Abhilash Nandy.
+
 > **Note**: If the Hugging Face dataset viewer is not available, you can still access the full dataset by downloading the CSV file directly or using the methods shown in the usage examples below.
 
 ## Dataset Summary
@@ -136,6 +107,12 @@ print(df.head())
 9. **Professional/Work-related**: <5%
 10. **Gross/Disgusting Events**: <5%
 
+## Research Paper & Code
+
+📄 **Paper**: ["Leveraging Large Language Models for Predictive Analysis of Human Misery"](https://arxiv.org/pdf/2508.12669)
+
+💻 **Code Repository**: [GitHub - Misery_Data_Exps_GitHub](https://github.com/abhi1nandy2/Misery_Data_Exps_GitHub)
+
 ## Data Sources
 
 The data was aggregated from three primary sources:
@@ -249,6 +226,17 @@ This dataset is valuable for:
 5. **Game Design**: Creating balanced difficulty curves in narrative games
 6. **Educational Tools**: Teaching empathy and emotional intelligence
 
+### Research Findings
+
+The associated research paper demonstrates several key findings:
+- **Few-shot prompting** significantly outperforms zero-shot approaches for misery prediction
+- **GPT-4o achieved the highest performance** with 61.79% accuracy in structured evaluation
+- **Binary comparisons** are easier for LLMs than precise scalar predictions (74.9% vs 43.1% accuracy)
+- **Retrieval-augmented prompting** using BERT embeddings improves prediction quality
+- **Feedback-driven adaptation** enables models to refine predictions iteratively
+
+The "Misery Game Show" evaluation framework introduced in the paper provides a novel approach to testing LLM capabilities in emotional reasoning tasks.
+
 ## Ethical Considerations
 
 - **Content Warning**: Dataset contains descriptions of distressing scenarios including accidents, medical emergencies, and personal tragedies
@@ -268,13 +256,25 @@ This dataset is valuable for:
 
 If you use this dataset in your research, please cite:
 
+```bibtex
+@article{seal2024leveraging,
+  title={Leveraging Large Language Models for Predictive Analysis of Human Misery},
+  author={Seal, Bishanka and Seetharaman, Rahul and Bansal, Aman and Nandy, Abhilash},
+  journal={arXiv preprint arXiv:2508.12669},
+  year={2024},
+  url={https://arxiv.org/pdf/2508.12669}
+}
+```
+
+For the dataset specifically:
 ```bibtex
 @dataset{misery_index_2024,
   title={Misery Index Dataset: Textual Scenarios with Emotional Distress Ratings},
-  author={[Author Names]},
+  author={Seal, Bishanka and Seetharaman, Rahul and Bansal, Aman and Nandy, Abhilash},
   year={2024},
   url={https://huggingface.co/datasets/path/to/misery-index},
-  note={Dataset of 516 scenarios with misery ratings from 0-100}
+  note={Dataset of 516 scenarios with misery ratings from 0-100},
+  howpublished={Hugging Face Datasets}
 }
 ```