# Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach

arXiv: https://arxiv.org/abs/2312.09591 · HTML: https://mlsanity.com/paper/2312.09591/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2023.

## Built on by (most cited first, 14 of 14)

- [CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation](https://mlsanity.com/paper/2406.17186/) (2024)
- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
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- [Logic Rules as Explanations for Legal Case Retrieval](https://mlsanity.com/paper/2403.01457/) (2024)
- [IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval](https://mlsanity.com/paper/2511.00268/) (2025)
- [AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios](https://mlsanity.com/paper/2505.16514/) (2025)
- [RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models](https://mlsanity.com/paper/2505.21281/) (2025)
- [ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework](https://mlsanity.com/paper/2506.18768/) (2025)
- [Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain](https://mlsanity.com/paper/2409.01357/) (2024)
- [A Method for Detecting Legal Article Competition for Korean Criminal Law Using a Case-augmented Mention Graph](https://mlsanity.com/paper/2412.11787/) (2024)
- [Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej](https://mlsanity.com/paper/2504.03486/) (2025)
- [PL-CA: A Parametric Legal Case Augmentation Framework](https://mlsanity.com/paper/2509.06356/) (2025)
- [JU'A -- A Benchmark for Information Retrieval in Brazilian Legal Text Collections](https://mlsanity.com/paper/2604.06098/) (2026)
- [Retrieval Augmented Generation Framework for the Nepali Legal Domain Question Answering](https://mlsanity.com/paper/2606.07523/) (2026)

## Abstract (arXiv)

Legal document retrieval and judgment prediction are crucial tasks in intelligent legal systems. In practice, determining whether two documents share the same judgments is essential for establishing their relevance in legal retrieval. However, existing legal retrieval studies either ignore the vital role of judgment prediction or rely on implicit training objectives, expecting a proper alignment of legal documents in vector space based on their judgments. Neither approach provides explicit evidence of judgment consistency for relevance modeling, leading to inaccuracies and a lack of transparency in retrieval. To address this issue, we propose a law-guided method, namely GEAR, within the generative retrieval framework. GEAR explicitly integrates judgment prediction with legal document retrieval in a sequence-to-sequence manner. Experiments on two Chinese legal case retrieval datasets show the superiority of GEAR over state-of-the-art methods while maintaining competitive judgment prediction performance. Moreover, we validate its robustness across languages and domains on a French statutory article retrieval dataset.