arXiv:2606.13931v1 Announce Type: new 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,
The proliferation of advanced LLMs necessitates more sophisticated evaluation benchmarks that address complex, multi-turn interactive capabilities beyond simple text generation or single-query responses, especially in critical domains like legal services.
This development indicates a crucial step towards making LLMs genuinely functional in high-stakes professional environments by measuring their ability to engage in nuanced, adaptive interactions, a key requirement for real-world application.
The focus of LLM evaluation is shifting from static task performance to dynamic, interactive competency, pushing model developers to build more adaptive and context-aware AI systems for expert domains.
- · Legal Tech Startups (AI-focused)
- · AI Agent Developers
- · Legal Professionals (adopting AI tools)
- · Clients (potentially receiving more accessible legal advice)
- · LLM Developers (who fail to adapt)
- · Traditional Legal Research Platforms
- · Legal Education Institutions (if they ignore AI-driven shifts)
DLawBench will spur the development of more interaction-capable LLMs specifically tailored for professional client-facing roles, starting with legal consultation.
Improved interactive LLMs could democratize access to basic legal information and advice, leading to a redefinition of entry-level legal services and paralegal tasks.
The success of multi-turn interactive LLMs in law could catalyze similar 'bench-to-application' breakthroughs in other complex, high-stakes service sectors currently dominated by human experts.
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Read at arXiv cs.CL