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arXiv · 2411.07228

ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving

Botao Yu·Frazier N. Baker·Ziru Chen·Garrett Herb·Boyu Gou·Daniel Adu-Ampratwum·Xia Ning·Huan Sun
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Abstract

To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemToolAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemToolAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help.

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Botao Yu, Frazier N. Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, Huan Sun. 2024-11-11. ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving. https://arxiv.org/abs/2411.07228

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