Title: ChemisTRAG: Table-based Retrieval-Augmented Generation for Chemistry Question Answering
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Chemistry, Retrieval-Augmented Generation, Large Language Model

Abstract: Recent work has shown that retrieval-augmented generation (RAG) improves the performance of large language models (LLMs) for question answering on chemistry. However, existing chemistry RAG techniques are mainly based on text. It is challenging for the retriever to align the information about chemical entities between the query and the underlying corpora, especially if the naming and representation formats change. To address this problem, we propose ChemisTRAG, a RAG system in which information about chemical entities and reactions is stored explicitly as tables in the knowledge base (KB). Upon a query, ChemisTRAG first extracts chemical entities from the query and then selects relevant rows from the tabular KB. This way, the alignment processing is simplified and the accuracy is improved regardless of different naming conventions of compounds. To balance accurate answer retrieval for exact matches and robust reasoning for similar matches, we propose an adaptive reasoning process for the LLM: it first generates a reasoning prototype, then adapts the reasoning path to retrieval results, and finally infers the final answer contextualized on the example reasoning path. We have constructed a dataset of more than 38,000 compounds and 23,000 reactions from the recent five years of patents, and generated eight types of question-answering tasks to evaluate our system. Results show that ChemisTRAG consistently outperforms text-based RAG across all eight tasks, particularly in handling diverse chemical representations like SMILES and IUPAC.

TL;DR: We propose ChemisTRAG, a table-based RAG system for chemistry question answering.