Summary of Lawma: the Power Of Specialization For Legal Tasks, by Ricardo Dominguez-olmedo et al.
Lawma: The Power of Specialization for Legal Tasks
by Ricardo Dominguez-Olmedo, Vedant Nanda, Rediet Abebe, Stefan Bechtold, Christoph Engel, Jens Frankenreiter, Krishna Gummadi, Moritz Hardt, Michael Livermore
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper investigates the application of large language models (LLMs) in empirical legal research, specifically for annotation and classification of legal text. Researchers typically rely on trained assistants for these tasks, but recent advances in LLMs have led to a shift towards utilizing commercial models. However, the effectiveness of this approach remains unclear. The authors conduct a comprehensive study of 260 legal text classification tasks, using GPT-4 as a baseline and comparing its performance to a lightly fine-tuned Llama 3 model. Results show that GPT-4 has variable zero-shot accuracy, while the fine-tuned Llama 3 model outperforms it by double-digit percentage points on almost all tasks. The study also finds that larger models respond better to fine-tuning and that a few tens to hundreds of examples are sufficient for achieving high classification accuracy. Moreover, the authors demonstrate that a single fine-tuned model can be used for multiple legal tasks simultaneously with only a small loss in accuracy relative to having separate models for each task. The study concludes that fine-tuning open-source models is a viable alternative to prompting commercial models for concrete legal tasks with some available labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can help lawyers and researchers understand and categorize legal texts. Traditionally, humans have done this work, but new AI technology has the potential to make it faster and cheaper. The study tests different computer models on 260 tasks of classifying legal texts and finds that a specific model called Llama 3 is much better than others at doing this job. The authors also discover that bigger AI models are more effective when trained for these tasks, and that using just a little bit of labeled data can help the models learn quickly. This research shows that instead of relying on expensive human labor, lawyers and researchers could use fine-tuned AI models to get accurate results. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Gpt » Llama » Prompting » Text classification » Zero shot
