@inproceedings{nigam-etal-2025-tathyanyaya,
    title = "{T}athya{N}yaya and {F}act{L}egal{L}lama: Advancing Factual Judgment Prediction and Explanation in the {I}ndian Legal Context",
    author = "Nigam, Shubham Kumar  and
      Patnaik, Balaramamahanthi Deepak  and
      Mishra, Shivam  and
      Shallum, Noel  and
      Ghosh, Kripabandhu  and
      Bhattacharya, Arnab",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.findings-ijcnlp.57/",
    pages = "985--1002",
    ISBN = "979-8-89176-303-6",
    abstract = "In the legal domain, Fact-based Judgment Prediction and Explanation (FJPE) aims to predict judicial outcomes and generate grounded explanations using only factual information, mirroring early-phase legal reasoning. Motivated by the overwhelming case backlog in the Indian judiciary, we introduce TathyaNyaya, the first large-scale, expert-annotated dataset for FJPE in the Indian context. Covering judgments from the Supreme Court and multiple High Courts, the dataset comprises four complementary components, NyayaFacts, NyayaScrape, NyayaSimplify, and NyayaFilter, that facilitate diverse factual modeling strategies. Alongside, we present FactLegalLlama, an instruction-tuned LLaMa-3-8B model fine-tuned to generate faithful, fact-grounded explanations. While FactLegalLlama trails transformer baselines in raw prediction accuracy, it excels in generating interpretable explanations, as validated by both automatic metrics and legal expert evaluation. Our findings show that fact-only inputs and preprocessing techniques like text simplification and fact filtering can improve both interpretability and predictive performance. Together, TathyaNyaya and FactLegalLlama establish a robust foundation for realistic, transparent, and trustworthy AI applications in the Indian legal system."
}