Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
This work addresses the problem of legal judgment prediction for the Indian legal and NLP communities, though it is incremental as it builds on existing LLM methods with a new dataset.
The authors tackled the challenge of predicting judicial outcomes in Indian courts by introducing PredEx, a large expert-annotated dataset with over 15,000 annotations, which improved AI model accuracy and explanatory depth for legal judgments.
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.