For retrieving prior cases in Indian legal judgments, the problem involves distinguishing relevant legal facts from mere lexical similarities because a prior case that shares a statute with the query judgment is not necessarily relevant. In this paper, we describe an empirical evaluation of three consecutive designs of...
Sayed Ayaan Ahmed Sha, Sangeetha Sivanesan, A. Madasamy et al.· 0 citations
TF-IDF provides a strong no-training lexical retrieval baseline, alongside representation analyses and generation controls for the learned models, and one narrow result remains: the decoder-only model prefers authentic word order in 107 of 112 minimal-pair comparisons, but does not reproduce held-out commentary content...
An end-to-end sequence-to-sequence formulation and fine-tune mT5-small and mBART-50 on a synthetic corpus of up to 657,720 noisy-clean Tamil sentence pairs spanning ten error categories, showing that a Tamil-adapted instruction model does not transfer to specialised sentence-level correction without task-specific super...
A. Karthikeyan, S. JayaNirmala, Sangeetha Sivanesan et al.· 0 citations
The findings show that the quality of the datasets and their alignment with the domain will greatly affect the performance of the model, that attention-based mechanisms can aid in explain ability, and that few-shot large language models can still produce structurally coherent translations of Tamil.
S. Sriharshaa, Sangeetha Sivanesan, S. Nirmala· arXiv.org· 0 citations
This research paper proposes a Retrieval Augmented Generation framework that is specific to the legal field in order to assist interactive retrieval and reason about judgments from the Supreme Court of India and demonstrates strong performance on metrics including contextual recall and answer relevancy.
Sayed Ayaan Ahmed Sha, Sangeetha Sivanesan, A. Madasamy et al.· 0 citations
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