title: ChemAgent: Self-updating Library in Large Language Models Improves
    Chemical Reasoning
summary: Chemical reasoning usually involves complex, multi-step processes that demand
  precise calculations, where even minor errors can lead to cascading failures.
  Furthermore, large language models (LLMs) encounter difficulties handling
  domain-specific formulas, executing reasoning steps accurately, and integrating
  code effectively when tackling chemical reasoning tasks. To address these
  challenges, we present ChemAgent, a novel framework designed to improve the
  performance of LLMs through a dynamic, self-updating library. This library is
  developed by decomposing chemical tasks into sub-tasks and compiling these
  sub-tasks into a structured collection that can be referenced for future
  queries. Then, when presented with a new problem, ChemAgent retrieves and
  refines pertinent information from the library, which we call memory,
  facilitating effective task decomposition and the generation of solutions. Our
  method designs three types of memory and a library-enhanced reasoning
  component, enabling LLMs to improve over time through experience. Experimental
  results on four chemical reasoning datasets from SciBench demonstrate that
  ChemAgent achieves performance gains of up to 46% (GPT-4), significantly
  outperforming existing methods. Our findings suggest substantial potential for
  future applications, including tasks such as drug discovery and materials
  science. Our code can be found at https://github.com/gersteinlab/chemagent
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paper_authors.fullname: Zhou
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paper_authors.name: Pan Lu
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paper_authors.fullname: Pan Lu
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paper_authors.user: lupantech
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paper_authors.name: Zhuosheng Zhang
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paper_authors.fullname: Arman Cohan
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paper_authors.name: Mark Gerstein
paper_title: ChemAgent: Self-updating Library in Large Language Models Improves
    Chemical Reasoning
paper_summary: Chemical reasoning usually involves complex, multi-step processes that demand
  precise calculations, where even minor errors can lead to cascading failures.
  Furthermore, large language models (LLMs) encounter difficulties handling
  domain-specific formulas, executing reasoning steps accurately, and integrating
  code effectively when tackling chemical reasoning tasks. To address these
  challenges, we present ChemAgent, a novel framework designed to improve the
  performance of LLMs through a dynamic, self-updating library. This library is
  developed by decomposing chemical tasks into sub-tasks and compiling these
  sub-tasks into a structured collection that can be referenced for future
  queries. Then, when presented with a new problem, ChemAgent retrieves and
  refines pertinent information from the library, which we call memory,
  facilitating effective task decomposition and the generation of solutions. Our
  method designs three types of memory and a library-enhanced reasoning
  component, enabling LLMs to improve over time through experience. Experimental
  results on four chemical reasoning datasets from SciBench demonstrate that
  ChemAgent achieves performance gains of up to 46% (GPT-4), significantly
  outperforming existing methods. Our findings suggest substantial potential for
  future applications, including tasks such as drug discovery and materials
  science. Our code can be found at https://github.com/gersteinlab/chemagent
paper_ai_keywords: large language models (LLMs) AutoAgent domain-specific formulas chemical reasoning tasks dynamic, self-updating library sub-tasks structured memory library-enhanced reasoning component SciBench drug discovery materials science