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.

在大型语言模型（LLMs）时代，提供了一个在印度背景下针对法律判决进行预测和解释的最大专家标注数据集（PredEx），该数据集具有超过15,000个注释。通过使用调整指令来提高LLMs的预测准确性和解释深度，我们表明这一方法显著提升了此类模型在法律判决上的预测准确性和解释深度，并且我们的模型通过严格的词法、语义和专家评估，有效地利用PredEx来提供精确的预测和有意义的解释，将其确定为法律专业和自然语言处理社区的有价值的基准。

重新想象的法律判断：PredEx与智能人工智能在印度法院的崛起