构建可调控的法律模拟人物，评估其行为真实度与教学价值。
Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation
- 用可控法律人格构建法庭陈述模拟器。
- 模拟人物行为符合真实边界，且不被轻易识破。
- 适合法律AI训练与司法教育场景使用。
庭审训练要求律师应对动态证人行为，但现有法律AI评估多聚焦事实准确性、推理或回应合理性。本文提出WitnessSim，一种由可控法律人格驱动的庭审模拟系统。通过对抗测试、盲测律师对比及纵向行为轨迹分析，分离评估行为真实度与教学实用性。结果表明，WitnessSim普遍维持合理的行为边界，律师无法系统性区分原始证词与生成内容。教学测试显示，证人行为能有效响应问题形式与律师干预，未完全崩溃预设人格。整体证明了法律模拟中行为保真度的实现，并提供可复用的评估框架。
原文摘要 · Abstract (English)
Deposition training requires attorneys to manage dynamic witness behavior, yet legal-AI evaluations largely focus on factual accuracy, reasoning, or response-level plausibility. We introduce WitnessSim, a deposition simulator driven by controllable legal personas. We use an evaluation framework separating behavioral realism from pedagogical usefulness. We assess realism through adversarial testing, blinded attorney comparison, and analysis of longitudinal behavioral trajectories. WitnessSim generally maintained plausible behavioral boundaries, and attorneys did not systematically prefer either original testimony or WitnessSim generated testimony. Pedagogical tests showed that witness behavior changed meaningfully in response to question form and attorney intervention without uniformly collapsing the assigned persona. Together, these results showcase a model of behavioral fidelity in legal simulations, and provide a framework for evaluating its performance.
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