  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.
