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

• URL: http://arxiv.org/abs/2504.05358v1
• Date: Mon, 07 Apr 2025 09:34:14 GMT
• Title: Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction
• Authors: Xi Chen, Mao Mao, Shuo Li, Haotian Shangguan,
• Abstract summary: We propose a novel legal judgment prediction model based on the Debate-Feedback architecture.<n>Unlike traditional methods, our model achieves significant improvements in efficiency by minimizing the need for large historical datasets.
• Score: 7.196065223124077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of AI in legal analysis and prediction (LegalAI) has gained widespread 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.

Related papers

• The Thinking Spectrum: An Empirical Study of Tunable Reasoning in LLMs through Model Merging [8.930191971732649]
  We present a large-scale empirical study evaluating a range of model merging techniques across multiple reasoning benchmarks.<n>Our findings reveal that model merging offers an effective and controllable method for calibrating the trade-off between reasoning accuracy and token efficiency.<n>Our study provides the first comprehensive analysis of this tunable space, offering practical guidelines for creating LLMs with specific reasoning profiles.
  arXiv  Detail & Related papers  (2025-09-26T08:12:13Z)
• FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization [19.69959362934787]
  We propose a new paradigm named FoMEMO, which enables the establishment of a foundation model conditioned on any domain trajectory and user preference.<n>Rather than accessing extensive domain experiments in the real world, we demonstrate that pre-training the foundation model with a diverse set of hundreds of millions of synthetic data can lead to superior adaptability to unknown problems.
  arXiv  Detail & Related papers  (2025-09-03T12:00:24Z)
• Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning [43.63419208391747]
  We present a novel framework that leverages expert-level optimization modeling principles through chain-of-thought reasoning to automate the optimization process.<n>We also introduce LogiOR, a new optimization modeling benchmark from the logistics domain, containing more complex problems with standardized annotations.
  arXiv  Detail & Related papers  (2025-08-20T04:14:54Z)
• Speed Always Wins: A Survey on Efficient Architectures for Large Language Models [51.817121227562964]
  Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models.<n> Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties.<n>The traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment.
  arXiv  Detail & Related papers  (2025-08-13T14:13:46Z)
• Discrete Diffusion in Large Language and Multimodal Models: A Survey [61.86669998363359]
  We provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs)<n>Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy.<n>We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, list commonly-used modeling methods, and categorize representative models.
  arXiv  Detail & Related papers  (2025-06-16T17:59:08Z)
• Debate, Reflect, and Distill: Multi-Agent Feedback with Tree-Structured Preference Optimization for Efficient Language Model Enhancement [43.532921045069365]
  Large Language Models (LLMs) continue to set new standards in knowledge-intensive and complex reasoning tasks.<n>Current techniques, such as static knowledge distillation, resource-intensive reinforcement learning from human feedback, or limited self-reflection to yield substantial and lasting performance gains.<n>We present a novel Reflect and Debate (D&R) framework that orchestrates multi-turn debates between smaller models and stronger teacher models, eliciting actionable feedback.
  arXiv  Detail & Related papers  (2025-06-04T03:52:20Z)
• Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws [52.10468229008941]
  This paper formalizes an emerging learning paradigm that uses a trained model as a reference to guide and enhance the training of a target model through strategic data selection or weighting.<n>We provide theoretical insights into why this approach improves generalization and data efficiency compared to training without a reference model.<n>Building on these insights, we introduce a novel method for Contrastive Language-Image Pretraining with a reference model, termed DRRho-CLIP.
  arXiv  Detail & Related papers  (2025-05-10T16:55:03Z)
• Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability [16.441081996257576]
  We propose leveraging reasoning-intensive models to improve less computationally demanding, non-reasoning models. We demonstrate consistent improvements across various benchmarks, underscoring the potential of this approach for advancing the ability of models to answer questions directly.
  arXiv  Detail & Related papers  (2025-04-13T16:26:56Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Exploring Training and Inference Scaling Laws in Generative Retrieval [50.82554729023865]
  We investigate how model size, training data scale, and inference-time compute jointly influence generative retrieval performance.<n>Our experiments show that n-gram-based methods demonstrate strong alignment with both training and inference scaling laws.<n>We find that LLaMA models consistently outperform T5 models, suggesting a particular advantage for larger decoder-only models in generative retrieval.
  arXiv  Detail & Related papers  (2025-03-24T17:59:03Z)
• A Survey of Direct Preference Optimization [103.59317151002693]
  Large Language Models (LLMs) have demonstrated unprecedented generative capabilities.<n>Their alignment with human values remains critical for ensuring helpful and harmless deployments.<n>Direct Preference Optimization (DPO) has recently gained prominence as a streamlined alternative.
  arXiv  Detail & Related papers  (2025-03-12T08:45:15Z)
• A Survey of Model Architectures in Information Retrieval [59.61734783818073]
  The period from 2019 to the present has represented one of the biggest paradigm shifts in information retrieval (IR) and natural language processing (NLP)<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude with a forward-looking discussion of emerging challenges and future directions.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• Adversarial Multi-Agent Evaluation of Large Language Models through Iterative Debates [0.0]
  We propose a framework that interprets large language models (LLMs) as advocates within an ensemble of interacting agents. This approach offers a more dynamic and comprehensive evaluation process compared to traditional human-based assessments or automated metrics.
  arXiv  Detail & Related papers  (2024-10-07T00:22:07Z)
• Multi-Reference Preference Optimization for Large Language Models [56.84730239046117]
  We introduce a novel closed-form formulation for direct preference optimization using multiple reference models. The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models. Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance.
  arXiv  Detail & Related papers  (2024-05-26T00:29:04Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language Models [21.438427686724932]
  We introduce a novel benchmark that assesses the predictive reasoning capabilities of MLLMs across diverse scenarios. Our benchmark targets three important domains: abstract pattern reasoning, human activity prediction, and physical interaction prediction. Empirical experiments confirm the soundness of the proposed benchmark and evaluation methods.
  arXiv  Detail & Related papers  (2023-10-20T13:14:38Z)
• Domain Generalization Using Large Pretrained Models with Mixture-of-Adapters [33.401355417911084]
  This study is on leveraging the knowledge of large pretrained models to improve handling of OOD scenarios and tackle domain generalization problems.<n>We employ parameter-efficient fine-tuning (PEFT) techniques to effectively preserve OOD robustness while working with large models.<n>Our experiments and analysis confirm that the most effective approaches involve ensembling diverse models and increasing the scale of pretraining.
  arXiv  Detail & Related papers  (2023-10-17T07:01:24Z)
• Scaling Vision-Language Models with Sparse Mixture of Experts [128.0882767889029]
  We show that mixture-of-experts (MoE) techniques can achieve state-of-the-art performance on a range of benchmarks over dense models of equivalent computational cost. Our research offers valuable insights into stabilizing the training of MoE models, understanding the impact of MoE on model interpretability, and balancing the trade-offs between compute performance when scaling vision-language models.
  arXiv  Detail & Related papers  (2023-03-13T16:00:31Z)

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.