DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation
- URL: http://arxiv.org/abs/2606.13931v1
- Date: Thu, 11 Jun 2026 21:50:44 GMT
- Title: DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation
- Abstract summary: We introduce DLawBench, a diagnostic benchmark for real-world legal consultation.<n>We characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial.<n>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.
- Score: 20.00846915357947
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial.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.
Related papers
- WisdomInterrogatory (LuWen): An Open-Source Legal Large Language Model Technical Report [55.27414605169639]
We present WisdomInterrogatory (LuWen), an open-source Chinese legal language model built upon the Baichuan foundation model.<n>We evaluate LuWen on five representative legal tasks spanning both prediction and generation settings.
arXiv Detail & Related papers (2026-04-08T06:59:07Z) - LegalOne: A Family of Foundation Models for Reliable Legal Reasoning [54.57434222018289]
We present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain.<n>LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning.<n>We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI.
arXiv Detail & Related papers (2026-01-31T10:18:32Z) - PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice [67.71760070255425]
We introduce PLawBench, a practical benchmark for evaluating large language models (LLMs) in legal practice scenarios.<n>PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics.<n>Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs.
arXiv Detail & Related papers (2026-01-23T11:36:10Z) - Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts [54.15982476754607]
Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks.<n>This study defines complicit facilitation as the provision of guidance or support that enables illicit user instructions.<n>Using real-world legal cases and established legal frameworks, we construct an evaluation benchmark spanning 269 illicit scenarios and 50 illicit intents.
arXiv Detail & Related papers (2025-11-25T16:01:31Z) - Assessing the Reliability of Large Language Models in the Bengali Legal Context: A Comparative Evaluation Using LLM-as-Judge and Legal Experts [0.0]
Generative AI models like OpenAI GPT-4.1 Mini, Gemini 2.0 Flash, Meta Llama 3 70B, and DeepSeek R1 could potentially democratize legal assistance.<n>In this study, we collected 250 authentic legal questions from the Facebook group "Know Your Rights"<n>We evaluated each AI-generated response across four critical dimensions: factual accuracy, legal appropriateness, completeness, and clarity.
arXiv Detail & Related papers (2025-11-07T02:44:00Z) - GLARE: Agentic Reasoning for Legal Judgment Prediction [60.13483016810707]
Legal judgment prediction (LJP) has become increasingly important in the legal field.<n>Existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge.<n>We introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules.
arXiv Detail & Related papers (2025-08-22T13:38:12Z) - LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation [68.26911563519162]
Legal consultation is essential for safeguarding individual rights and ensuring access to justice.<n>Current systems fall short in handling the interactive and knowledge-intensive nature of real-world consultations.<n>LeCoDe is a real-world multi-turn benchmark dataset comprising 3,696 legal consultation dialogues with 110,008 dialogue turns.
arXiv Detail & Related papers (2025-05-26T08:24:32Z) - LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation [1.9857357818932064]
LawLuo is a multi-agent framework for multi-turn Chinese legal consultations.<n>LawLuo includes four agents: the receptionist agent, which assesses user intent and selects a lawyer agent; the lawyer agent, which interacts with the user; the secretary agent, which organizes conversation records and generates consultation reports.<n>These agents' interactions mimic the operations of real law firms.
arXiv Detail & Related papers (2024-07-23T07:40:41Z) - LegalBench: A Collaboratively Built Benchmark for Measuring Legal
Reasoning in Large Language Models [15.98468948605927]
LegalBench is a benchmark consisting of 162 tasks covering six different types of legal reasoning.
This paper describes LegalBench, presents an empirical evaluation of 20 open-source and commercial LLMs, and illustrates the types of research explorations LegalBench enables.
arXiv Detail & Related papers (2023-08-20T22:08:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.