Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction (2025)

Abstract: The use of AI in legal analysis and prediction (LegalAI) has gained attention, with past research focusing on retrieval-based methods and fine-tuning large models. However, these approaches often require large datasets and underutilize the capabilities of modern large language models (LLMs). In this paper, inspired by the debate phase of real courtroom trials, we propose a novel legal judgment prediction model based on the Debate-Feedback architecture, which integrates LLM multi-agent debate and reliability evaluation models. Unlike traditional methods, our model achieves significant improvements in efficiency by minimizing the need for large historical datasets, thus offering a lightweight yet robust solution. Comparative experiments show that it outperforms several general-purpose and domain-specific legal models, offering a dynamic reasoning process and a promising direction for future LegalAI research.

## introduction legalai leverages artificial intelligence technologies such as natural language processing, machine learning, and deep learning to address various legal tasks @xcite @xcite , including legal document analysis and consultation. a key area of legalai is legal judgment prediction (ljp) @xcite @xcite , which focuses on predicting court judgments. ljp tasks typically use historical legal case data, including background information, case descriptions, statements from both parties, precedents, and court verdicts. predictions range from binary outcomes (e.g., plaintiff vs. defendant wins) to multi-class tasks (e.g., sentence prediction). nlp technologies, combined with advanced models like legalbert @xcite and lawformer @xcite , have achieved strong results by learning from large datasets. the debate model is a system that integrates large language modeling (llm) with argumentative reasoning techniques to simulate the process of debate or contention @xcite , ultimately arriving at a decision or conclusion on a specific issue through the debate process. in a typical debate task, multiple llm agents assume different roles and are deliberately guided to provide answers from various perspectives or positions. these generated arguments are then synthesized to assist the llm in reaching a final conclusion @xcite . in this paper, we propose a debate-feedback model to explore an efficient and convenient method for predicting legal judgement. fig @xcite shows the general framework of the model in the task of predicting decision results. specifically, debate-feedback can be divided into four steps. first, the collected historical legal cases l i will be formatted into case background c i , plaintiff claim p i and defendant statement d i . these information will be provided to the judge llm for initial prediction. in the second step of the debate, multiple llm agents will be guided to answer the prediction questions from different perspectives, and then exchange opinions and debate to generate their own comments e i . in the verification phase, a pre-trained assistant model e will conduct a reliability analysis on each llm's comments combined with case information. the results of the analysis will be provided to the judge llm for reference together with each agent's comments. the judge llm will give the prediction o i for this round based on the above information @xmath0. more details are illustrated in the methodology section. in summary, we introduce a debate-feedback model that enhances legal judgment prediction by incorporating a multi-agent debate process and reliability evaluation, providing a more efficient and accurate solution with reduced reliance on large datasets. ## related work legal documents are characterized by lengthy texts and complex logic, which has led prior research to focus on two key approaches to address these challenges: training legal llm and using retrieval augmentation. ## methodology in this section, we first systematically introduce our feedback-debate model, followed by an analysis of the limitations of the general debate architecture in specific legal scenarios, along with proposed solutions to address these shortcomings. overview algorithm[1] presents the pseudo code for the debate-feedback framework in binary classification. the input is a preprocessed legal event text, labeled as s, and the main language model (lm) plays the role of the judge, predicting the probability of a legal judgment, lm : @xmath1, 1]. two agents, t ne and t po , debate from opposing perspectives, providing inputs to refine the judgment. each debate round involves these agents exchanging and debating their positions, with n defining the number of iterations. the assistant model e evaluates the reliability of the agents' arguments and outputs a probability. if the reliability exceeds a threshold, the main lm adjusts its prediction by weighting the latest information, otherwise it defaults to the initial prediction. the final decision is smoothed over all algorithm 1: debate-feedback input: lm, e : @xmath2, 1]; n, @xmath3; @xmath4; t ne , t po : @xmath5; output: final decision @xmath6, 1); ## conclusion we propose a debate-feedback model based on llms for legal judgment prediction and demonstrated its feasibility through experiments. the inclusion of an assistant model and reliability analysis enhances prediction robustness. future work could explore the application of debate models in other fields or further integrate them with llms.