# Human Centered AI for Indian Legal Text Analytics

arXiv: https://arxiv.org/abs/2403.10944 · HTML: https://mlsanity.com/paper/2403.10944/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories](https://mlsanity.com/paper/2306.05406/) (2023)
- [Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey](https://mlsanity.com/paper/2110.08455/) (2021)
- [SKILL: Structured Knowledge Infusion for Large Language Models](https://mlsanity.com/paper/2205.08184/) (2022)
- [Corpus for Automatic Structuring of Legal Documents](https://mlsanity.com/paper/2201.13125/) (2022)
- [Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation](https://mlsanity.com/paper/2210.07544/) (2022)
- [CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL](https://mlsanity.com/paper/2311.01173/) (2023)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text](https://mlsanity.com/paper/2108.08614/) (2021)
- [JuriBERT: A Masked-Language Model Adaptation for French Legal Text](https://mlsanity.com/paper/2110.01485/) (2021)
- [Legal Summarisation through LLMs: The PRODIGIT Project](https://mlsanity.com/paper/2308.04416/) (2023)
- [Similar Cases Recommendation using Legal Knowledge Graphs](https://mlsanity.com/paper/2107.04771/) (2021)
- [There is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering](https://mlsanity.com/paper/2301.04013/) (2023)
- [ConcEPT: Concept-Enhanced Pre-Training for Language Models](https://mlsanity.com/paper/2401.05669/) (2024)
- [Infusing Knowledge into Large Language Models with Contextual Prompts](https://mlsanity.com/paper/2403.01481/) (2024)

## Built on by (most cited first, 1 of 1)

- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)

## Abstract (arXiv)

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.