# CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation

arXiv: https://arxiv.org/abs/2502.17943 · HTML: https://mlsanity.com/paper/2502.17943/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://mlsanity.com/paper/2305.08322/) (2023)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Large Language Models in Law: A Survey](https://mlsanity.com/paper/2312.03718/) (2023)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [LegalAgentBench: Evaluating LLM Agents in Legal Domain](https://mlsanity.com/paper/2412.17259/) (2024)
- [LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://mlsanity.com/paper/2310.17609/) (2023)
- [CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges](https://mlsanity.com/paper/2410.15393/) (2024)
- [BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models](https://mlsanity.com/paper/2403.18365/) (2024)
- [LAiW: A Chinese Legal Large Language Models Benchmark](https://mlsanity.com/paper/2310.05620/) (2023)
- [DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment](https://mlsanity.com/paper/2403.18435/) (2024)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval](https://mlsanity.com/paper/2404.00947/) (2024)

## Built on by (most cited first, 9 of 9)

- [A Survey on Large Language Model Benchmarks](https://mlsanity.com/paper/2508.15361/) (2025)
- [Enhancing LLM-Based Agents via Global Planning and Hierarchical Execution](https://mlsanity.com/paper/2504.16563/) (2025)
- [GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations](https://mlsanity.com/paper/2505.17267/) (2025)
- [LegalOne: A Family of Foundation Models for Reliable Legal Reasoning](https://mlsanity.com/paper/2602.00642/) (2026)
- [LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks](https://mlsanity.com/paper/2606.09389/) (2026)
- [AppellateGen: A Benchmark for Appellate Legal Judgment Generation](https://mlsanity.com/paper/2601.01331/) (2026)
- [CLASE: A Hybrid Method for Chinese Legalese Stylistic Evaluation](https://mlsanity.com/paper/2602.12639/) (2026)
- [Na Pr'atica, qual IA Entende o Direito? Um Estudo Experimental com IAs Generalistas e uma IA Jur'idica](https://mlsanity.com/paper/2510.18108/) (2025)
- [Civil Court Simulation with Large Language Models](https://mlsanity.com/paper/2606.09632/) (2026)

## Abstract (arXiv)

Legal case documents play a critical role in judicial proceedings. As the number of cases continues to rise, the reliance on manual drafting of legal case documents is facing increasing pressure and challenges. The development of large language models (LLMs) offers a promising solution for automating document generation. However, existing benchmarks fail to fully capture the complexities involved in drafting legal case documents in real-world scenarios. To address this gap, we introduce CaseGen, the benchmark for multi-stage legal case documents generation in the Chinese legal domain. CaseGen is based on 500 real case samples annotated by legal experts and covers seven essential case sections. It supports four key tasks: drafting defense statements, writing trial facts, composing legal reasoning, and generating judgment results. To the best of our knowledge, CaseGen is the first benchmark designed to evaluate LLMs in the context of legal case document generation. To ensure an accurate and comprehensive evaluation, we design the LLM-as-a-judge evaluation framework and validate its effectiveness through human annotations. We evaluate several widely used general-domain LLMs and legal-specific LLMs, highlighting their limitations in case document generation and pinpointing areas for potential improvement. This work marks a step toward a more effective framework for automating legal case documents drafting, paving the way for the reliable application of AI in the legal field. The dataset and code are publicly available at https://github.com/CSHaitao/CaseGen.