The paper releases the AusLaw Citation Benchmark and shows that instruction-tuned 7B-8B LLMs plus retrieval re-ranking outperform general and law-specific pretrained LLMs for legal citation prediction, reaching about 52% accuracy with a large gap remaining.
IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning
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abstract
Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and understanding legal documents to streamline the legal system. However, evaluating and comparing various NLP models designed specifically for the legal domain is challenging. This paper addresses this challenge by proposing IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning. IL-TUR contains monolingual (English, Hindi) and multi-lingual (9 Indian languages) domain-specific tasks that address different aspects of the legal system from the point of view of understanding and reasoning over Indian legal documents. We present baseline models (including LLM-based) for each task, outlining the gap between models and the ground truth. To foster further research in the legal domain, we create a leaderboard (available at: https://exploration-lab.github.io/IL-TUR/) where the research community can upload and compare legal text understanding systems.
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Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study
The paper releases the AusLaw Citation Benchmark and shows that instruction-tuned 7B-8B LLMs plus retrieval re-ranking outperform general and law-specific pretrained LLMs for legal citation prediction, reaching about 52% accuracy with a large gap remaining.