# RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models

arXiv: https://arxiv.org/abs/2505.21281 · HTML: https://mlsanity.com/paper/2505.21281/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services](https://mlsanity.com/paper/2309.11325/) (2023)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023)
- [Description Based Text Classification with Reinforcement Learning](https://mlsanity.com/paper/2002.03067/) (2020)
- [Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction](https://mlsanity.com/paper/2407.01964/) (2024)
- [Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach](https://mlsanity.com/paper/2312.09591/) (2023)
- [Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction](https://mlsanity.com/paper/2211.08238/) (2022)
- [LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset](https://mlsanity.com/paper/2403.04791/) (2024)
- [Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge](https://mlsanity.com/paper/2408.09422/) (2024)
- [SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgment Prediction with Legal Clue Tracing](https://mlsanity.com/paper/2408.09717/) (2024)

## Built on by (most cited first, 3 of 3)

- [The Judge Variable: Challenging Judge-Agnostic Legal Judgment Prediction](https://mlsanity.com/paper/2507.13732/) (2025)
- [Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction](https://mlsanity.com/paper/2601.12688/) (2026)
- [Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts](https://mlsanity.com/paper/2604.17674/) (2026)

## Abstract (arXiv)

Legal Judgment Prediction (LJP) is a pivotal task in legal AI. Existing semantic-enhanced LJP models integrate judicial precedents and legal knowledge for high performance. But they neglect legal reasoning logic, a critical component of legal judgments requiring rigorous logical analysis. Although some approaches utilize legal reasoning logic for high-quality predictions, their logic rigidity hinders adaptation to case-specific logical frameworks, particularly in complex cases that are lengthy and detailed. This paper proposes a rule-enhanced legal judgment prediction framework based on first-order logic (FOL) formalism and comparative learning (CL) to develop an adaptive adjustment mechanism for legal judgment logic and further enhance performance in LJP. Inspired by the process of human exam preparation, our method follows a three-stage approach: first, we initialize judgment rules using the FOL formalism to capture complex reasoning logic accurately; next, we propose a Confusion-aware Contrastive Learning (CACL) to dynamically optimize the judgment rules through a quiz consisting of confusable cases; finally, we utilize the optimized judgment rules to predict legal judgments. Experimental results on two public datasets show superior performance across all metrics. The code is publicly available{https://anonymous.4open.science/r/RLJP-FDF1}.