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README.md

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 # BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
 
-Reference: R. Luu and M.J. Buehler, Adv. Science, 2023, DOI: https://doi.org/10.1002/advs.202306724
+Reference: R. Luu and M.J. Buehler, "BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials," Adv. Science, 2023, DOI: https://doi.org/10.1002/advs.202306724
 
 Abstract: The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains. 
 
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 #### Bias, Risks, and Limitations
 
+Like in all techniques of modeling, there are possibilities of errors. The base models Llama 2 and Orca 2 models were aligned to not spread misinformation and produce safer responses. As a result, BioinspiredLLM has inherited these traits and performs reasonably well in these dimensions. However, it is still of utmost importance for researchers to also verify responses and avoid propagating errors, as discussed in recent literature[64] – a standard practice across all modeling techniques. To minimize risk of mistakes, employing chain-of-thought prompting and RAG methods, as introduced, proves beneficial. Additionally, the system prompt of BioinspiredLLM can be edited to guide context. Further details see the main paper.
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 This model, built upon the LLaMA 2 and Orca-2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including:
 
 Data Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair.