工具还是没有工具? 工具对化学问题解决的语言代理的影响
Botao Yu1, Frazier N Baker1,2, Ziru Chen1
1Department of Computer Science and Engineering, OSU.
概括
用工具增强的大型语言模型 (LLM) 在化学问题解决方面显示出混合的结果. 专门的工具有助于完成特定的任务,但对于广泛的化学问题,一般的推理是关键.
科学领域:
- 人工智能的人工智能
- 计算化学计算化学
- 化学教育 化学教育
背景情况:
- 大型语言模型 (LLM) 越来越多地用于科学问题解决.
- 现有的基于LLM的代理 (例如ChemCrow,Coscientist) 在各种化学任务中进行的评估有限.
- 缺乏对化学LLM工具增强效益的全面了解.
研究的目的:
- 开发和评估ChemAgent,一种用于化学问题解决的增强型LLM代理.
- 在专业和一般化学任务中进行全面的绩效评估.
- 为了确定工具增强对化学LLM绩效的影响.
主要方法:
- 开发ChemAgent,这是一个基于ChemCrow的LLM代理.
- 在专门的化学任务上对ChemAgent进行全面评估 (例如,合成预测).
- 在一般化学问题上评估ChemAgent,包括考试式问题.
- 与化学专家一起进行错误分析,以了解性能限制.
主要成果:
- 在没有工具增强的情况下,ChemAgent的表现并不始终超过其基本的大型语言模型.
- 工具增强对合成预测等专业化学任务有好处.
- 对于一般化学问题,LLM与化学知识固有的推理能力比工具使用更为关键.
- 工具增强并不总是提高一般化学问题的表现.
结论:
- 对基于LLM的化学剂的工具增强的有效性取决于任务.
- 专门的工具对于特定的化学任务是有益的,例如反应合成.
- 对于一般化学知识评估,提高LLM的核心推理能力至关重要.
- 需要进一步的研究来优化各种化学问题解决场景的LLM代理.
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