Summary of Methods For Legal Citation Prediction in the Age Of Llms: An Australian Law Case Study, by Ehsan Shareghi et al.
Methods for Legal Citation Prediction in the Age of LLMs: An Australian Law Case Study
by Ehsan Shareghi, Jiuzhou Han, Paul Burgess
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates Large Language Models (LLMs) for legal citation prediction within the Australian law context. Despite their potential, state-of-the-art LLMs still frequently generate incorrect legal references due to hallucination. The authors compare various approaches: prompting general-purpose and law-specialized LLMs, retrieval-only pipelines with generic and domain-specific embeddings, task-specific instruction-tuning of LLMs, and hybrid strategies combining LLMs with retrieval augmentation, query expansion, or voting ensembles. Findings indicate that domain-specific pre-training alone is insufficient for achieving satisfactory citation accuracy, but instruction tuning on a task-specific dataset dramatically boosts performance. Hybrid methods consistently outperform retrieval-only setups, and ensemble voting delivers the best result by combining instruction-tuned LLMs with retrieval systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using big language models to help lawyers correctly identify and cite relevant laws in Australia. Right now, these models can make mistakes by suggesting wrong laws or references. The researchers compared different ways of using these models for citation prediction: teaching them specific law-related tasks, using specialized embeddings for legal text, and combining multiple models together. They found that training the models to do a specific task made a big difference in their accuracy. They also showed that combining multiple models together can lead to even better results. |
Keywords
» Artificial intelligence » Hallucination » Instruction tuning » Prompting
