Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909218483732480 |
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| author | Nigam, Shubham Kumar Sharma, Anurag Khanna, Danush Shallum, Noel Ghosh, Kripabandhu Bhattacharya, Arnab |
| author_facet | Nigam, Shubham Kumar Sharma, Anurag Khanna, Danush Shallum, Noel Ghosh, Kripabandhu Bhattacharya, Arnab |
| contents | In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce \textbf{Pred}iction with \textbf{Ex}planation (\texttt{PredEx}), the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context, featuring over 15,000 annotations. This groundbreaking corpus significantly enhances the training and evaluation of AI models in legal analysis, with innovations including the application of instruction tuning to LLMs. This method has markedly improved the predictive accuracy and explanatory depth of these models for legal judgments. We employed various transformer-based models, tailored for both general and Indian legal contexts. Through rigorous lexical, semantic, and expert assessments, our models effectively leverage \texttt{PredEx} to provide precise predictions and meaningful explanations, establishing it as a valuable benchmark for both the legal profession and the NLP community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04136 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts Nigam, Shubham Kumar Sharma, Anurag Khanna, Danush Shallum, Noel Ghosh, Kripabandhu Bhattacharya, Arnab Computation and Language Artificial Intelligence Machine Learning In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce \textbf{Pred}iction with \textbf{Ex}planation (\texttt{PredEx}), the largest expert-annotated dataset for legal judgment prediction and explanation in the Indian context, featuring over 15,000 annotations. This groundbreaking corpus significantly enhances the training and evaluation of AI models in legal analysis, with innovations including the application of instruction tuning to LLMs. This method has markedly improved the predictive accuracy and explanatory depth of these models for legal judgments. We employed various transformer-based models, tailored for both general and Indian legal contexts. Through rigorous lexical, semantic, and expert assessments, our models effectively leverage \texttt{PredEx} to provide precise predictions and meaningful explanations, establishing it as a valuable benchmark for both the legal profession and the NLP community. |
| title | Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.04136 |