@inproceedings{zhang-etal-2026-dlawbench,
title = "{DL}aw{B}ench: Evaluating {LLM}s Through Multi-Turn Legal Consultation",
author = "Zhang, Li and
Shi, Yuzhen and
HU, Yiran and
Jingwen, Zhang and
Lv, Wenbo and
Ma, Yubo and
Wang, Wei and
Shi, Rongyao and
Qiu, Yuanyang and
Xinran, Xu and
Qi, Yuemeng and
Miao, Linlin and
Savelka, Jaromir and
Liu, Yun and
Ashley, Kevin D. and
Zhao, Bing and
Wei, HU and
Qu, Lin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1403/",
pages = "32053--32091",
ISBN = "979-8-89176-459-0",
abstract = "Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order to devise strategies that best protect their interests. This task requires Large Language Models (LLMs) not only to perform robust legal reasoning, but also to strategically elicit material facts through multi-turn interactions and effectively guide clients with diverse personalities. Yet existing legal benchmarks overlook this interactive capability. To fill this gap, we introduce DLawBench, a diagnostic benchmark for real-world legal consultation. Drawing on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial. Using dialogues grounded in real cases, DLawBench evaluates whether LLMs can effectively conduct legal consultation under realistic conditions. DLawBench comprises 461 cases from Chinese and U.S. law, 5,532 paired fact entries, 3,411 inquiry rubrics, and 3,348 issue-resolution rubrics, and evaluates 26 representative LLMs. Systematic experiments show substantial headroom: the best-performing model, GPT-5.5, achieves only 0.562 on consultation-grounded legal reasoning. More importantly, DLawBench exposes both sycophancy in legal consultation and a paradox: models perform worse when clients need guidance most."
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<abstract>Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order to devise strategies that best protect their interests. This task requires Large Language Models (LLMs) not only to perform robust legal reasoning, but also to strategically elicit material facts through multi-turn interactions and effectively guide clients with diverse personalities. Yet existing legal benchmarks overlook this interactive capability. To fill this gap, we introduce DLawBench, a diagnostic benchmark for real-world legal consultation. Drawing on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial. Using dialogues grounded in real cases, DLawBench evaluates whether LLMs can effectively conduct legal consultation under realistic conditions. DLawBench comprises 461 cases from Chinese and U.S. law, 5,532 paired fact entries, 3,411 inquiry rubrics, and 3,348 issue-resolution rubrics, and evaluates 26 representative LLMs. Systematic experiments show substantial headroom: the best-performing model, GPT-5.5, achieves only 0.562 on consultation-grounded legal reasoning. More importantly, DLawBench exposes both sycophancy in legal consultation and a paradox: models perform worse when clients need guidance most.</abstract>
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%0 Conference Proceedings
%T DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation
%A Zhang, Li
%A Shi, Yuzhen
%A HU, Yiran
%A Jingwen, Zhang
%A Lv, Wenbo
%A Ma, Yubo
%A Wang, Wei
%A Shi, Rongyao
%A Qiu, Yuanyang
%A Xinran, Xu
%A Qi, Yuemeng
%A Miao, Linlin
%A Savelka, Jaromir
%A Liu, Yun
%A Ashley, Kevin D.
%A Zhao, Bing
%A Wei, H. U.
%A Qu, Lin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-dlawbench
%X Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order to devise strategies that best protect their interests. This task requires Large Language Models (LLMs) not only to perform robust legal reasoning, but also to strategically elicit material facts through multi-turn interactions and effectively guide clients with diverse personalities. Yet existing legal benchmarks overlook this interactive capability. To fill this gap, we introduce DLawBench, a diagnostic benchmark for real-world legal consultation. Drawing on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial. Using dialogues grounded in real cases, DLawBench evaluates whether LLMs can effectively conduct legal consultation under realistic conditions. DLawBench comprises 461 cases from Chinese and U.S. law, 5,532 paired fact entries, 3,411 inquiry rubrics, and 3,348 issue-resolution rubrics, and evaluates 26 representative LLMs. Systematic experiments show substantial headroom: the best-performing model, GPT-5.5, achieves only 0.562 on consultation-grounded legal reasoning. More importantly, DLawBench exposes both sycophancy in legal consultation and a paradox: models perform worse when clients need guidance most.
%U https://aclanthology.org/2026.emnlp-main.1403/
%P 32053-32091
Markdown (Informal)
[DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation](https://aclanthology.org/2026.emnlp-main.1403/) (Zhang et al., EMNLP 2026)
ACL
- Li Zhang, Yuzhen Shi, Yiran HU, Zhang Jingwen, Wenbo Lv, Yubo Ma, Wei Wang, Rongyao Shi, Yuanyang Qiu, Xu Xinran, Yuemeng Qi, Linlin Miao, Jaromir Savelka, Yun Liu, Kevin D. Ashley, Bing Zhao, HU Wei, and Lin Qu. 2026. DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32053–32091, Budapest, Hungary. Association for Computational Linguistics.