Summary of Legal Judgment Reimagined: Predex and the Rise Of Intelligent Ai Interpretation in Indian Courts, by Shubham Kumar Nigam et al.
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
by Shubham Kumar Nigam, Anurag Sharma, Danush Khanna, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
First submitted to arxiv on: 6 Jun 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 research paper introduces PredEx, the largest expert-annotated dataset for predicting judicial outcomes in India. The dataset features over 15,000 annotations and is designed to enhance the training and evaluation of AI models in legal analysis. The authors apply instruction tuning to Large Language Models (LLMs) to improve predictive accuracy and explanatory depth. Various transformer-based models are employed, tailored for both general and Indian legal contexts. The paper establishes PredEx as a valuable benchmark for the legal profession and the NLP community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps predict judicial outcomes by creating a big dataset with expert annotations. It’s like training AI to make smart guesses about court decisions. The team uses special language models to improve accuracy and provide explanations. They test these models on different types of legal cases, making it easier for judges, lawyers, and researchers to understand how courts work. |
Keywords
» Artificial intelligence » Instruction tuning » Nlp » Transformer
