AI & ML
A Study of Prior Case Retrieval Using Lexical, Semantic, and Rhetorical Role Information in Indian Legal Documents
arXiv cs.IR0 views
arXiv:2610.07437v1 Announce Type: new Abstract: 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 retrieval systems for the IL PCR(Indian Legal Prior Case Retrieval) task. We demonstrate that the combination of the rhetorical roles (Fact, Ratio Of The Decision, Precedent, Argument, Statute) is better than either using individual roles or performing full text retrieval, that statute similarity is not discriminative, and that a legal entailment reranker with training data produced by an LLM is much worse in terms of official evaluation than its internal validation score. Two rankers, with excellent internal MRR up to 0.97, performed poorly in terms of official evaluation (MRR as low as 0.14). Thus, we propose a new design with query disjoint splitting and frozen validation fusion. The result is a four stage pipeline with Micro F1 0.2549, MRR 0.6081, and nDCG@10 0.4187.
Source:https://arxiv.org/abs/2610.07437PreviousRethinking Semantic ID Construction for Generative Recommendation: SimHash with Parallel Decoding and Semantic AlignmentNextDBRAG: Multi-Table Retrieval-Augmented Generation for Complex Database Queries
Related posts

Build World Models for Interactive Simulation with NVIDIA Cosmos
0NVIDIA Developer Blog
Seeing and Imagining as One: Unified Video Generation and 4D Perception for Embodied World Modeling
0arXiv cs.RO
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
0arXiv cs.RO
BeeTalk 群主必备:AI 群管机器人 AIGB 免费解决垃圾信息问题
0dev.to AI
I Open-Sourced a Free YouTube Shorts Factory. The Money Isn't in the Shorts.
0dev.to AI
Priced per TB/s, the H200 beats the H100 at 6 of 7 providers
0dev.to Python