Summary of Augmenting Legal Decision Support Systems with Llm-based Nli For Analyzing Social Media Evidence, by Ram Mohan Rao Kadiyala et al.
Augmenting Legal Decision Support Systems with LLM-based NLI for Analyzing Social Media Evidence
by Ram Mohan Rao Kadiyala, Siddartha Pullakhandam, Kanwal Mehreen, Subhasya Tippareddy, Ashay Srivastava
First submitted to arxiv on: 21 Oct 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 abstract presents our system description and error analysis of our entry for the 2024 shared task on Legal Natural Language Inference (L-NLI). The L-NLI task requires classifying relationships between reviews and complaints as entailed, contradicted, or neutral. Our winning submission significantly outperformed other entries with a substantial margin, demonstrating the effectiveness of our approach in legal text analysis. We analyze the strengths and limitations of each model and approach tested, provide error analysis, and suggest future improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to understand relationships between texts in law. Imagine reading two paragraphs – one a review, and the other a complaint. Can you tell if they agree or disagree? That’s what this task is all about! Our team did really well, beating others with a big margin. This shows that our way of doing things works well for understanding legal text. The paper talks about how we did it and why it matters. |
Keywords
» Artificial intelligence » Inference
