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English(EN) PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models

新的PORTS方法提高了LLM工具选择的准确性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了PORTS，一种用于训练检索器以更好地为大语言模型（LLM）选择工具的新方法。现有的检索器由于训练过程分离，常常与LLM不匹配。PORTS使用带有冻结LLM的偏好优化技术来微调检索器，通过将选择概率与下游性能相关联来提高其查找有用工具的能力。这种方法在各种数据集和LLM上都显著提高了工具选择的准确性，计算需求低，并且具有良好的泛化能力，适用于实际应用。 AI

影响 通过提高LLM有效选择和利用外部工具的能力来增强LLM的功能。

排序理由 该集群包含一篇详细介绍改进LLM工具选择新方法的论文。

在 arXiv cs.AI 阅读 →

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新的PORTS方法提高了LLM工具选择的准确性

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Lorenzo Molfetta, Giacomo Frisoni, Nicol\`o Monaldini, Gianluca Moro ·

    PORTS：面向大型语言模型的、经偏好优化的工具选择检索器

    arXiv:2607.05441v1 Announce Type: cross Abstract: Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still struggle to effectively manage large tool collections, researchers have begun explo…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Gianluca Moro ·

    PORTS：面向大型语言模型工具选择的偏好优化检索器

    Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still struggle to effectively manage large tool collections, researchers have begun exploring retrieval-based methods to pre-select the mos…

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