# PL-CA: A Parametric Legal Case Augmentation Framework

arXiv: https://arxiv.org/abs/2509.06356 · HTML: https://mlsanity.com/paper/2509.06356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 8 Sep 2025.

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

- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2408.10343/) (2024)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases](https://mlsanity.com/paper/2403.10446/) (2024)
- [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)
- [LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://mlsanity.com/paper/2305.07507/) (2023)
- [AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://mlsanity.com/paper/2408.08089/) (2024)
- [Are Long-LLMs A Necessity For Long-Context Tasks?](https://mlsanity.com/paper/2405.15318/) (2024)
- [Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction](https://mlsanity.com/paper/2502.06882/) (2025)
- [JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System](https://mlsanity.com/paper/2503.14258/) (2025)
- [Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach](https://mlsanity.com/paper/2312.09591/) (2023)
- [LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation](https://mlsanity.com/paper/2407.16252/) (2024)
- [Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs](https://mlsanity.com/paper/2410.06581/) (2024)
- [An Evaluation Framework for Legal Document Summarization](https://mlsanity.com/paper/2205.08478/) (2022)
- [DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment](https://mlsanity.com/paper/2403.18435/) (2024)
- [Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents](https://mlsanity.com/paper/2505.14104/) (2025)
- [ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework](https://mlsanity.com/paper/2506.18768/) (2025)

## Abstract (arXiv)

Conventional RAG is considered one of the most effective methods for addressing model knowledge insufficiency and hallucination, particularly in the judicial domain that requires high levels of knowledge rigor, logical consistency, and content integrity. However, the conventional RAG method only injects retrieved documents directly into the model's context, which severely constrains models due to their limited context windows and introduces additional computational overhead through excessively long contexts, thereby disrupting models' attention and degrading performance on downstream tasks. Moreover, many existing benchmarks lack expert annotation and focus solely on individual downstream tasks while real-world legal scenarios consist of multiple mixed legal tasks, indicating conventional benchmarks' inadequacy for reflecting models' true capabilities. To address these limitations, we propose PL-CA, which introduces a parametric RAG (P-RAG) framework to perform data augmentation on corpus knowledge and encode this legal knowledge into parametric vectors, and then integrates this parametric knowledge into the LLM's feed-forward networks (FFN) via LoRA, thereby alleviating models' context pressure. Additionally, we also construct a multi-task legal dataset comprising more than 2000 training and test instances, which are all expert-annotated and manually verified. We conduct our experiments on our dataset, and the experimental results demonstrate that our method reduces the overhead associated with excessively long contexts while maintaining competitive performance on downstream tasks compared to conventional RAG. Our code and dataset are provided in the appendix.