  The integration of generative Large Language Models (LLMs) into various
applications, including the legal domain, has been accelerated by their
expansive and versatile nature. However, when facing a legal case, users
without a legal background often struggle to formulate professional queries and
may inadvertently overlook critical legal factors when presenting their case
narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large
Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions
to collect additional case information and then provides high-quality feedback.
D3LM incorporates an innovative graph-based Positive-Unlabeled Reinforcement
Learning (PURL) algorithm, enabling the generation of critical questions and
enhancing user-LLM interactions. Moreover, an integrated LLM-based stopping
criterion facilitates precise Court Views Generation (CVG). Our research also
introduces a new English-language CVG dataset based on the US case law
database, enriching the realm of LLM research and deployment with a vital
dimension. D3LM surpasses classical LLMs by delivering outstanding performance
and a remarkable user experience in the legal domain.
