LexPilot: Benchmarking the Proactive Interaction Ability of Legal LLMs with Laypeople in Chinese Civil Case Consultation
To address this gap, we introduce LexPilot, a benchmark linking proactive legal interaction to claim-level verdict prediction using authentic Chinese civil judgments.
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Key points
- Large Language Models (LLMs) have greatly assisted legal professionals.
- However, our empirical studies reveal that, when faced with underspecified consultations from non-experts, current LLMs often produce irresponsible responses with limited utility despite lacking sufficient case details for definitive conclusions.
- To better assist laypeople, legal LLMs must identify focal points of dispute and proactively elicit crucial context from users.
- Unfortunately, existing data resources often focus on interaction and response quality, offering limited insight into how proactive interaction affects LLMs' downstream claim-level judgment prediction in actual civil cases.
Sources (1)
- [1]LexPilot: Benchmarking the Proactive Interaction Ability of Legal LLMs with Laypeople in Chinese Civil Case ConsultationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 08:38 AM
To address this gap, we introduce LexPilot, a benchmark linking proactive legal interaction to claim-level verdict prediction using authentic Chinese civil judgments.
Large Language Models (LLMs) have greatly assisted legal professionals.
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