mlsanityData to 7 Oct 2026

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding

arXiv 2301.00876 ↗First posted 2 Jan 2023cs.CLcited by 32

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Abstract (arXiv)

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 2 Jan 2023.

Builds on

19 of 28 references.

References · 19

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7 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 12; 2026: 13.

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  1. 2023Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Textscited by 32
  2. 2023Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?cited by 12
  3. 2025Towards Reliable Retrieval in RAG Systems for Large Legal Datasetscited by 12
  4. 2024LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domaincited by 10
  5. 2025Large Language Models Meet Legal Artificial Intelligence: A Surveycited by 9
  6. 2025LeMAJ (Legal LLM-as-a-Judge): Bridging Legal Reasoning and LLM Evaluationcited by 9
  7. 2026Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understandingcited by 8
  8. 2024Lawma: The Power of Specialization for Legal Annotationcited by 7
  9. 2025AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliancecited by 5
  10. 2025sudoLLM: On Multi-role Alignment of Language Modelscited by 4
  11. 2025Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generationcited by 4
  12. 2025AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMscited by 4
20 more
  1. 2026RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Gamecited by 3
  2. 2024Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4cited by 2
  3. 2025LegalBench.PT: A Benchmark for Portuguese Lawcited by 2
  4. 2025Computational Law: Datasets, Benchmarks, and Ontologiescited by 2
  5. 2025CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Modelscited by 2
  6. 2026CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Lawcited by 2
  7. 2023RISC: Generating Realistic Synthetic Bilingual Insurance Contractcited by 1
  8. 2024Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Studycited by 1
  9. 2025PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreementscited by 1
  10. 2025The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasurecited by 1
  11. 2026Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directionscited by 1
  12. 2026Scaling Multi-Hop Training Data via Graph-Constrained Path Selectioncited by 1
  13. 2026Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewardscited by 1
  14. 2026Capital Markets LLM Reliability Score (CM-LRS): From Plausible to Bankablecited by 0
  15. 2026Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documentscited by 0
  16. 2026On Measuring Semantic Preservation in Legal Ontology Learningcited by 0
  17. 2026ContractScrub: A benchmark for final review of legal contractscited by 0
  18. 2026Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?cited by 0
  19. 2026Credo: Reusable Declarative Primitives for Agentic Workflowscited by 0
  20. 2026What the Window Does Not Contain: Auditing Provenance in a Document-Grounded Instability Benchmarkcited by 0