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Human Centered AI for Indian Legal Text Analytics

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arxiv 2403.10944 v1 pith:4TCPBFHU submitted 2024-03-16 cs.HC cs.AI

Sudipto Ghosh , Devanshu Verma , Balaji Ganesan , Purnima Bindal , Vikas Kumar , Vasudha Bhatnagar This is my paper · ORCID
classification cs.HCcs.AI
keywords humanlegalllmsanalyticscenteredresearchtextapplications
verification ladder T0 review T1 audit T2 compute T3 formal
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Legal research is a crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. Recent boom in generative AI has not translated to proportionate rise in impactful legal applications, because of low trustworthiness and and the scarcity of specialized datasets for training Large Language Models (LLMs). This position paper explores the potential of LLMs within Legal Text Analytics (LTA), highlighting specific areas where the integration of human expertise can significantly enhance their performance to match that of experts. We introduce a novel dataset and describe a human centered, compound AI system that principally incorporates human inputs for performing LTA tasks with LLMs.

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  1. When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A literature review that classifies LLM-for-law research using a dual-lens taxonomy of Toulmin argumentation components and legal practitioner roles.

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