Data Augmented Pipeline for Legal Information Extraction and Reasoning
On the map: LLM reasoning → Reasoning for agents, planning and code
Abstract (arXiv)
In this paper, we propose a pipeline leveraging Large Language Models (LLMs) for data augmentation in Information Extraction tasks within the legal domain. The proposed method is both simple and effective, significantly reducing the manual effort required for data annotation while enhancing the robustness of Information Extraction systems. Furthermore, the method is generalizable, making it applicable to various Natural Language Processing (NLP) tasks beyond the legal domain.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 2 of its 5 references that are in the map. First posted on arXiv 9 Jan 2026.
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2 of 5 references.
References · 2
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2016Data Recombination for Neural Semantic Parsingcited by 111
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- 2023One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
- 2023LawBench: Benchmarking Legal Knowledge of Large Language Models
- 2024KRAG Framework for Enhancing LLMs in the Legal Domain
- 2025LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
- 2026Enhancing Legal LLMs through Metadata-Enriched RAG Pipelines and Direct Preference Optimization
- 2025When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
- 2023Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement
- 2024LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain