让大模型学会根据用户习惯推荐工具，提升个性化服务体验。
PEToolLLM: Towards Personalized Tool Learning in Large Language Models
- 基于用户历史交互构建个性化工具学习框架
- 在新基准上性能超越现有大模型，提升明显
- 适合需要懂用户习惯的智能助手开发者
工具学习通过拓展大语言模型（LLMs）的外部工具调用能力成为新兴方向。现有研究多聚焦通用工具使用，仅响应显式指令，忽略用户隐含偏好。为此，本文首次提出个性化工具学习任务，融合用户交互历史以实现个性化工具推荐。为填补评测空白，构建了包含三类个性化设置的PEToolBench基准，涵盖多样工具使用场景。同时提出PEToolLLaMA框架，通过监督微调与直接偏好优化训练。在PEToolBench上的大量实验表明，该框架显著优于现有大模型。
原文摘要 · Abstract (English)
Tool learning has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on the general-purpose tool-use capability, which addresses explicit user requirements in instructions. However, they overlook the importance of personalized tool-use capability, leading to an inability to handle implicit user preferences. To address the limitation, we first formulate the task of personalized tool learning, which integrates user's interaction history towards personalized tool usage. To fill the gap of missing benchmarks, we construct PEToolBench, featuring diverse user preferences reflected in interaction history under three distinct personalized settings, and encompassing a wide range of tool-use scenarios. Moreover, we propose a framework PEToolLLaMA to adapt LLMs to the personalized tool learning task, which is trained through supervised fine-tuning and direct preference optimization. Extensive experiments on PEToolBench demonstrate the superiority of PEToolLLaMA over existing LLMs.
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