Non-Collaborative User Simulators for Tool Agents
Jeonghoon Shim, Woojung Song, Cheyon Jin, Seungwon KooK, Yohan Jo
摘要
Tool agents interact with users through multi-turn dialogues to accomplish various tasks. Recent studies have adopted user simulation methods to develop these agents in multi-turn settings. However, existing user simulators tend to be agent-friendly, exhibiting only cooperative behaviors, failing to train and test agents against non-collaborative users in the real world. We propose a novel user simulator architecture that simulates four categories of non-collaborative behaviors: requesting unavailable services, digressing into tangential conversations, expressing impatience, and providing incomplete utterances. Our user simulator can simulate challenging and natural non-collaborative behaviors while reliably delivering all intents and information necessary to accomplish the task. Our experiments on MultiWOZ and -bench reveal significant performance degradation in state-of-the-art tool agents when encountering non-collaborative users, as well as agent weaknesses under each non-collaborative condition such as escalated hallucinations and dialogue breakdowns. Our findings point to the need for methods that can improve agent robustness to the wide range of user behaviors encountered in deployment. We release the extensible simulation framework to help the community develop and stress-test tool agents under realistic conditions within their own service domains. Our code is available at https://github.com/holi-lab/NCUser.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper8
- QLoRA: Efficient Finetuning of Quantized LLMsTim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke ZettlemoyerNeurIPS 2023 · 被引用 5,863 次
- Gorilla: Large Language Model Connected with Massive APIsShishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. GonzalezNeurIPS 2024 · 被引用 1,715 次
- LLMs Get Lost In Multi-Turn ConversationPhilippe Laban, Hiroaki Hayashi, Yingbo Zhou, Jennifer NevilleICLR 2026 · 被引用 491 次
- MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language FeedbackXingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen 等ICLR 2024 · 被引用 308 次
- API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMsMinghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song 等EMNLP 2023 · 被引用 72 次
相关 Paper
- -Bench: Evaluating Conversational Agents in a Dual-Control EnvironmentVictor Barres, Honghua Dong, Soham Ray, Xujie Si 等ICML 2026 · 被引用 399 次
- Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing AgentsMuyu He, Anand Kumar, Soumyadeep Bakshi, James Zou 等ACL 2026 · 被引用 8 次
- Analyzing and Simulating User Utterance Reformulation in Conversational Recommender SystemsShuo Zhang, Mu-Chun Wang, Krisztian BalogSIGIR 2022 · 被引用 18 次
- Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language ModelsLujain Ibrahim, Canfer Akbulut, Rasmi Elasmar, Charvi Rastogi 等ICLR 2026 · 被引用 40 次
- CoCo: Controllable Counterfactuals for Evaluating Dialogue State TrackersShiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li 等ICLR 2021 · 被引用 65 次
