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While integrating external tools into large language models (LLMs) enhances their ability to access real-time information and domain-specific services, existing approaches focus narrowly on functional tool selection following user instructions, overlooking the context-aware personalization in tool selection.
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Llama 3 model card
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LaMP: When large language models meet personalization
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CharacterEval: A Chinese benchmark for role-playing conversational agent evaluation
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FlowBench: Revisiting and benchmarking workflow-guided planning for LLM-based agents
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RoTBench: A multi-level benchmark for evaluating the robustness of large language models in tool learning
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InjecAgent: Benchmarking indirect prompt injections in tool-integrated large language model agents
Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. 2024 · 2024
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ToolBeHonest: A multi-level hallucination diagnostic benchmark for tool-augmented large language models
Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, and Hayato Yamana. 2024a · 2024
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Cognitive personalized search integrating large language models with an efficient memory mechanism
Yujia Zhou, Qiannan Zhu, Jiajie Jin, and Zhicheng Dou. 2024 · 2024
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NesTools: A dataset for evaluating nested tool learning abilities of large language models
Han Han, Tong Zhu, Xiang Zhang, MengSong Wu, Xiong Hao, and Wenliang Chen. 2025 · 2025
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Smart: Self-aware agent for tool overuse mitigation
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Self-DC: When to reason and when to act? self divide-and-conquer for compositional unknown questions
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