# Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models

arXiv: https://arxiv.org/abs/2309.14735 · HTML: https://mlsanity.com/paper/2309.14735/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering](https://mlsanity.com/paper/1908.08167/) (2019)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://mlsanity.com/paper/1911.00841/) (2019)
- [BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA](https://mlsanity.com/paper/2005.00766/) (2020)
- [A Survey on Legal Question Answering Systems](https://mlsanity.com/paper/2110.07333/) (2021)
- [Expert Finding in Legal Community Question Answering](https://mlsanity.com/paper/2201.07667/) (2022)
- [Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog](https://mlsanity.com/paper/2112.08327/) (2021)

## Built on by (most cited first, 10 of 10)

- [Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models](https://mlsanity.com/paper/2410.10542/) (2024)
- [LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification](https://mlsanity.com/paper/2502.05836/) (2025)
- [NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System](https://mlsanity.com/paper/2508.00709/) (2025)
- [NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis](https://mlsanity.com/paper/2412.08385/) (2024)
- [From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation](https://mlsanity.com/paper/2604.10470/) (2026)
- [Assessing the Reliability of Large Language Models in the Bengali Legal Context: A Comparative Evaluation Using LLM-as-Judge and Legal Experts](https://mlsanity.com/paper/2511.05627/) (2025)
- [TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context](https://mlsanity.com/paper/2504.04737/) (2025)
- [Long-Context Long-Form Question Answering for Legal Domain](https://mlsanity.com/paper/2602.07190/) (2026)
- [Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej](https://mlsanity.com/paper/2504.03486/) (2025)
- [Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments](https://mlsanity.com/paper/2608.06828/) (2026)

## Abstract (arXiv)

Legal QA platforms bear the promise to metamorphose the manner in which legal experts engage with jurisprudential documents. In this exposition, we embark on a comparative exploration of contemporary AI frameworks, gauging their adeptness in catering to the unique demands of the Indian legal milieu, with a keen emphasis on Indian Legal Question Answering (AILQA). Our discourse zeroes in on an array of retrieval and QA mechanisms, positioning the OpenAI GPT model as a reference point. The findings underscore the proficiency of prevailing AILQA paradigms in decoding natural language prompts and churning out precise responses. The ambit of this study is tethered to the Indian criminal legal landscape, distinguished by its intricate nature and associated logistical constraints. To ensure a holistic evaluation, we juxtapose empirical metrics with insights garnered from seasoned legal practitioners, thereby painting a comprehensive picture of AI's potential and challenges within the realm of Indian legal QA.