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    <abstract>Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs). Existing works predominantly focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models’ proactive dialogue abilities. In this work, we propose ProactiveEval, a unified framework for evaluating proactive dialogue capabilities of LLMs. This framework decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains. Moreover, it also enables the automatic generation of diverse and challenging evaluation data. Based on the proposed framework, we develop 328 evaluation environments spanning 6 distinct domains. Through experiments with 22 different types of LLMs, we show that DeepSeek-R1 and Claude-3.7-Sonnet exhibit exceptional performance on target planning and dialogue guidance tasks, respectively. Finally, we investigate how reasoning capabilities influence proactive behaviors and discuss their implications for future model development. Our code and data are available at the https://github.com/liutj9/ProactiveEval.</abstract>
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%0 Conference Proceedings
%T ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
%A Liu, Tianjian
%A Wan, Fanqi
%A Guo, Jiajian
%A Quan, Xiaojun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F liu-etal-2026-proactiveeval
%X Proactive dialogue has emerged as a critical and challenging research problem in advancing large language models (LLMs). Existing works predominantly focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models’ proactive dialogue abilities. In this work, we propose ProactiveEval, a unified framework for evaluating proactive dialogue capabilities of LLMs. This framework decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains. Moreover, it also enables the automatic generation of diverse and challenging evaluation data. Based on the proposed framework, we develop 328 evaluation environments spanning 6 distinct domains. Through experiments with 22 different types of LLMs, we show that DeepSeek-R1 and Claude-3.7-Sonnet exhibit exceptional performance on target planning and dialogue guidance tasks, respectively. Finally, we investigate how reasoning capabilities influence proactive behaviors and discuss their implications for future model development. Our code and data are available at the https://github.com/liutj9/ProactiveEval.
%R 10.18653/v1/2026.acl-long.1906
%U https://aclanthology.org/2026.acl-long.1906/
%U https://doi.org/10.18653/v1/2026.acl-long.1906
%P 41068-41100

Markdown (Informal)

[ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents](https://aclanthology.org/2026.acl-long.1906/) (Liu et al., ACL 2026)

• ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents (Liu et al., ACL 2026)

ACL

• Tianjian Liu, Fanqi Wan, Jiajian Guo, and Xiaojun Quan. 2026. ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 41068–41100, San Diego, California, United States. Association for Computational Linguistics.