研究人员开发了CoHyDE，一种新颖的迭代协同训练方法，旨在增强LLM代理的工具检索能力。该方法联合训练一个密集编码器和一个LLM重写器，解决了用户口语化查询与技术API目录之间的词汇不匹配问题。CoHyDE通过使编码器和重写器协同进化并更好地与工具目录对齐，展示了显著的改进，尤其是在模糊查询方面。 AI
影响 增强了LLM代理理解和利用复杂API目录的能力，可能提高了其现实世界任务的完成度。
排序理由 该集群描述了一篇介绍改进LLM代理能力新方法的最新研究论文。
在 Hugging Face Daily Papers 阅读 →
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arXiv:2605.29271v1 Announce Type: new Abstract: Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the catalog uses technical API vocabulary that no fixed encoder can bridge on its ow…
Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the catalog uses technical API vocabulary that no fixed encoder can bridge on its own. The two dominant training approaches, contras…
CoHyDE is an iterative method that jointly trains a dense encoder and LLM rewriter to improve tool retrieval from API catalogs, achieving better performance on both specific and vague queries through co-evolutionary training.