AI-lead Court Debate Case Investigation

• URL: http://arxiv.org/abs/2010.11604v2
• Date: Mon, 16 Nov 2020 03:47:33 GMT
• Title: AI-lead Court Debate Case Investigation
• Abstract summary: In the judicial trial, it can help the judge raise efficient questions so that the judge has a clearer understanding of the case. In this work, we propose an innovative end-to-end question generation model-Trial Brain Model (TBM) to build a Trial Brain.
• Score: 24.221021061471177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial. Different from other types of dialogue, questions are raised by the judge, The plaintiff, plaintiff's agent defendant, and defendant's agent would be to debating so that the trial can proceed in an orderly manner. Question generation is an important task in Natural Language Generation. In the judicial trial, it can help the judge raise efficient questions so that the judge has a clearer understanding of the case. In this work, we propose an innovative end-to-end question generation model-Trial Brain Model (TBM) to build a Trial Brain, it can generate the questions the judge wants to ask through the historical dialogue between the plaintiff and the defendant. Unlike prior efforts in natural language generation, our model can learn the judge's questioning intention through predefined knowledge. We do experiments on real-world datasets, the experimental results show that our model can provide a more accurate question in the multi-role court debate scene.

Related papers

• When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors [2.8935588665357077]
  We study when and how a defendant's courtroom statement affects LLM-simulated jurors.<n>JuryBench is a benchmark containing controversial criminal cases in U.S. criminal law.<n>We find that emotional persuasion can be detrimental, since jurors may perceive it as evidence of guilt or inconsistency.<n>Background fit between jurors and defendants is a stronger and significant factor than other isolated factors.
  arXiv  Detail & Related papers  (2026-09-09T08:43:34Z)
• CyberJurors: A Multi-Agent Simulation Task for E-Commerce Disputes Verdict [59.06704011432714]
  E-commerce platforms have begun recruiting crowdsourced jurors to adjudicate massive volumes of transaction disputes.<n>We introduce a pioneering task, E-commerce Dispute Verdicts (EDV), and present VerdictBench, a multimodal benchmark comprising 6,000 real-world cases.<n>We propose CyberJurors, a multi-agent framework to clarify the dispute logic and regulate the verdict process.
  arXiv  Detail & Related papers  (2026-05-27T12:07:44Z)
• AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments [7.808898285349819]
  We examine whether AI models can effectively simulate justice-specific questioning for moot court-style training.<n>We construct and evaluate both prompt-based and agentic oral argument simulators.<n>We find that simulated questions are often perceived as realistic by human annotators and achieve high recall of ground truth substantive legal issues.
  arXiv  Detail & Related papers  (2026-03-05T01:45:28Z)
• The Silicon Reasonable Person: Can AI Predict How Ordinary People Judge Reasonableness? [0.0]
  This Article investigates whether large language models (LLMs) can learn to identify patterns driving human reasonableness judgments.<n>We show that certain models capture not just surface-level responses but potentially their underlying decisional architecture.<n>These findings suggest practical applications: judges could calibrate intuitions against broader patterns, lawmakers could test policy interpretations, and resource-constrained litigants could preview argument reception.
  arXiv  Detail & Related papers  (2025-08-04T06:19:45Z)
• AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios [47.83822985839837]
  We present the AppealCase dataset, consisting of 10,000 pairs of real-world, matched first-instance and second-instance documents across 91 categories of civil cases.<n>The dataset also includes detailed annotations along five dimensions central to appellate review: judgment reversals, reversal reasons, cited legal provisions, claim-level decisions, and whether there is new information in the second instance.<n> Experimental results reveal that all current models achieve less than 50% F1 scores on the judgment reversal prediction task, highlighting the complexity and challenge of the appeal scenario.
  arXiv  Detail & Related papers  (2025-05-22T10:50:33Z)
• JudgeBench: A Benchmark for Evaluating LLM-based Judges [61.048125269475854]
  JudgeBench is a benchmark for evaluating LLM-based judges on challenging response pairs spanning knowledge, reasoning, math, and coding. Our comprehensive evaluation on a collection of prompted judges, fine-tuned judges, multi-agent judges, and reward models shows that JudgeBench poses a significantly greater challenge than previous benchmarks.
  arXiv  Detail & Related papers  (2024-10-16T17:58:19Z)
• On scalable oversight with weak LLMs judging strong LLMs [67.8628575615614]
  We study debate, where two AI's compete to convince a judge; consultancy, where a single AI tries to convince a judge that asks questions. We use large language models (LLMs) as both AI agents and as stand-ins for human judges, taking the judge models to be weaker than agent models.
  arXiv  Detail & Related papers  (2024-07-05T16:29:15Z)
• Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM [51.43102092480804]
  Debatrix is an automated debate judge based on Large Language Models (LLMs) To align with real-world debate scenarios, we introduced the PanelBench benchmark, comparing our system's performance to actual debate outcomes. The findings indicate a notable enhancement over directly using LLMs for debate evaluation.
  arXiv  Detail & Related papers  (2024-03-12T18:19:47Z)
• AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation [19.733007669738008]
  We propose a novel multi-agent framework, AgentsCourt, for judicial decision-making. Our framework follows the classic court trial process, consisting of court debate simulation, legal resources retrieval and decision-making refinement. To support this task, we construct a large-scale legal knowledge base, Legal-KB, with multi-resource legal knowledge.
  arXiv  Detail & Related papers  (2024-03-05T13:30:02Z)
• Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning [49.23103067844278]
  We propose the task of multi-defendant LJP, which aims to automatically predict the judgment results for each defendant of multi-defendant cases. Two challenges arise with the task of multi-defendant LJP: (1) indistinguishable judgment results among various defendants; and (2) the lack of a real-world dataset for training and evaluation.
  arXiv  Detail & Related papers  (2023-12-10T04:46:30Z)
• The Ethics of Automating Legal Actors [58.81546227716182]
  We argue that automating the role of the judge raises difficult ethical challenges, in particular for common law legal systems. Our argument follows from the social role of the judge in actively shaping the law, rather than merely applying it. Even in the case the models could achieve human-level capabilities, there would still be remaining ethical concerns inherent in the automation of the legal process.
  arXiv  Detail & Related papers  (2023-12-01T13:48:46Z)
• A Computational Analysis of Oral Argument in the Supreme Court [0.0]
  The basic function and operation of oral argument as an institution remains poorly understood. This Article uses machine learning techniques to construct predictive models of judicial decision making. It offers an important new window into aspects of oral argument that have long resisted empirical study.
  arXiv  Detail & Related papers  (2023-06-05T13:52:56Z)
• JUSTICE: A Benchmark Dataset for Supreme Court's Judgment Prediction [0.0]
  We aim to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment.
  arXiv  Detail & Related papers  (2021-12-06T23:19:08Z)
• Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting [25.53133777558123]
  We introduce a novel dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction.
  arXiv  Detail & Related papers  (2021-07-12T04:27:14Z)
• What About the Precedent: An Information-Theoretic Analysis of Common Law [64.49276556192073]
  In common law, the outcome of a new case is determined mostly by precedent cases, rather than existing statutes. We are the first to approach this question by comparing two longstanding jurisprudential views. We find that the precedent's arguments share 0.38 nats of information with the case's outcome, whereas precedent's facts only share 0.18 nats of information.
  arXiv  Detail & Related papers  (2021-04-25T11:20:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.