Summary of Exploring Large Language Models and Hierarchical Frameworks For Classification Of Large Unstructured Legal Documents, by Nishchal Prasad et al.
Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal Documents
by Nishchal Prasad, Mohand Boughanem, Taoufiq Dkaki
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A deep-learning-based hierarchical framework called MESc is proposed for predicting legal judgments from large, unstructured case documents. The framework, which combines embeddings from the last four layers of a fine-tuned Large Language Model with unsupervised clustering, achieves a minimum total performance gain of approximately 2 points over previous state-of-the-art methods on ILDC and LexGLUE datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Legal judgments can be difficult to predict due to long, unstructured case documents. A new approach called MESc uses a deep-learning-based hierarchical framework to improve judgment prediction. This method combines the last four layers of a fine-tuned Large Language Model with unsupervised clustering to better understand document structure and content. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Large language model » Unsupervised
