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English(EN) How Many Tools Should an LLM Agent See? A Chance-Corrected Answer

新指标优化 LLM 代理工具选择以提高准确性

研究人员开发了一种名为 Bits-over-Random (BoR) 的新机会校正指标,用于确定 LLM 代理在给定查询时应考虑的最佳工具数量。该指标评估在特定工具短名单深度下的成功率是否优于随机几率。研究发现,即使与固定、较长的列表相比,选项数量少得多,但针对每个查询量身定制的自适应工具列表也能显著提高 LLM 选择正确工具的能力。 AI

影响 优化 LLM 代理的工具选择可以提高 AI 系统中任务完成的效率和准确性。

排序理由 在 arXiv 上发表的研究论文,详细介绍了 LLM 工具选择的新指标。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新指标优化 LLM 代理工具选择以提高准确性

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在 arXiv 上发表的研究论文,详细介绍了 LLM 工具选择的新指标。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh, Joey Blackwell II ·

    LLM Agent应拥有多少工具?一个机会校正的答案

    arXiv:2605.24660v1 Announce Type: cross Abstract: Before an LLM agent can use a tool, a retrieval system must decide which candidate tools to show to the agent. How long should that shortlist be? Show too many tools and the model struggles to choose. Show too few and the correct …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Joey Blackwell ·

    LLM Agent应拥有多少工具?一个机会校正的答案

    Before an LLM agent can use a tool, a retrieval system must decide which candidate tools to show to the agent. How long should that shortlist be? Show too many tools and the model struggles to choose. Show too few and the correct tool may not appear. Most systems apply a fixed sh…