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The paper introduces a novel law reasoning schema incorporating hierarchical factum probandum, evidence, and implicit experience to enhance transparency and mitigate bias in legal applications of LLMs. To evaluate this schema, the authors created a challenging task that requires LLMs to generate a hierarchical structure justifying a legal decision based on a textual case description, along with a corresponding crowd-sourced dataset. They also propose an agent framework leveraging legal analysis tools to address the challenge, establishing a benchmark for transparent and accountable AI-assisted law reasoning.
LLMs can now be held accountable for legal reasoning via a new benchmark that demands transparent, hierarchical justifications for their decisions.
While progress has been made in legal applications, law reasoning, crucial for fair adjudication, remains unexplored. We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience, enabling public scrutiny and preventing bias. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. We also create the first crowd-sourced dataset for this task, enabling comprehensive evaluation. Simultaneously, we propose an agent framework that employs a comprehensive suite of legal analysis tools to address the challenge task. This benchmark paves the way for transparent and accountable AI-assisted law reasoning in the ``Intelligent Court''.
