Long-Context Long-Form Question Answering for Legal Domain
Not on the map. Why?
Abstract (arXiv)
Legal documents have complex document layouts involving multiple nested sections, lengthy footnotes and further use specialized linguistic devices like intricate syntax and domain-specific vocabulary to ensure precision and authority. These inherent characteristics of legal documents make question answering challenging, and particularly so when the answer to the question spans several pages (i.e. requires long-context) and is required to be comprehensive (i.e. a long-form answer). In this paper, we address the challenges of long-context question answering in context of long-form answers given the idiosyncrasies of legal documents. We propose a question answering system that can (a) deconstruct domain-specific vocabulary for better retrieval from source documents, (b) parse complex document layouts while isolating sections and footnotes and linking them appropriately, (c) generate comprehensive answers using precise domain-specific vocabulary. We also introduce a coverage metric that classifies the performance into recall-based coverage categories allowing human users to evaluate the recall with ease. We curate a QA dataset by leveraging the expertise of professionals from fields such as law and corporate tax. Through comprehensive experiments and ablation studies, we demonstrate the usability and merit of the proposed system.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 6 Feb 2026.
Builds on
8 of 17 references.
References · 8
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
- 2024LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answeringcited by 49
- 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 47
- 2024DMQR-RAG: Diverse Multi-Query Rewriting for RAGcited by 26
- 2023Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Modelscited by 10
- 2025Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understandingcited by 6
Built on by
Citing papers per year: 2026: 1.
1 paper.