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

arXiv: https://arxiv.org/abs/2301.00876 · HTML: https://mlsanity.com/paper/2301.00876/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [SCROLLS: Standardized CompaRison Over Long Language Sequences](https://mlsanity.com/paper/2201.03533/) (2022)
- [ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts](https://mlsanity.com/paper/2110.01799/) (2021)
- [CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service](https://mlsanity.com/paper/1805.01217/) (2018)
- [CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension](https://mlsanity.com/paper/1912.09156/) (2019)
- [A Benchmark for Lease Contract Review](https://mlsanity.com/paper/2010.10386/) (2020)
- [Obligation and Prohibition Extraction Using Hierarchical RNNs](https://mlsanity.com/paper/1805.03871/) (2018)

## Built on by (most cited first, 30 of 33)

- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?](https://mlsanity.com/paper/2310.14880/) (2023)
- [Towards Reliable Retrieval in RAG Systems for Large Legal Datasets](https://mlsanity.com/paper/2510.06999/) (2025)
- [Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding](https://mlsanity.com/paper/2609.27678/) (2026)
- [LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain](https://mlsanity.com/paper/2404.02127/) (2024)
- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
- [LeMAJ (Legal LLM-as-a-Judge): Bridging Legal Reasoning and LLM Evaluation](https://mlsanity.com/paper/2510.07243/) (2025)
- [Lawma: The Power of Specialization for Legal Annotation](https://mlsanity.com/paper/2407.16615/) (2024)
- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [sudoLLM: On Multi-role Alignment of Language Models](https://mlsanity.com/paper/2505.14607/) (2025)
- [Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generation](https://mlsanity.com/paper/2505.19804/) (2025)
- [AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs](https://mlsanity.com/paper/2507.18584/) (2025)
- [RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game](https://mlsanity.com/paper/2604.10740/) (2026)
- [Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4](https://mlsanity.com/paper/2404.01961/) (2024)
- [LegalBench.PT: A Benchmark for Portuguese Law](https://mlsanity.com/paper/2502.16357/) (2025)
- [Computational Law: Datasets, Benchmarks, and Ontologies](https://mlsanity.com/paper/2503.04305/) (2025)
- [CHANCERY: Evaluating Corporate Governance Reasoning Capabilities in Language Models](https://mlsanity.com/paper/2506.04636/) (2025)
- [CanLegalRAGBench: Evaluating Retrieval-Augmented Generation on Canadian Case Law](https://mlsanity.com/paper/2605.30497/) (2026)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study](https://mlsanity.com/paper/2412.06272/) (2024)
- [PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements](https://mlsanity.com/paper/2506.00608/) (2025)
- [The Medium Is Not the Message: Deconfounding Document Embeddings via Linear Concept Erasure](https://mlsanity.com/paper/2507.01234/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [Scaling Multi-Hop Training Data via Graph-Constrained Path Selection](https://mlsanity.com/paper/2605.31238/) (2026)
- [Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2607.14506/) (2026)
- [ContractScrub: A benchmark for final review of legal contracts](https://mlsanity.com/paper/2608.20204/) (2026)
- [Capital Markets LLM Reliability Score (CM-LRS): From Plausible to Bankable](https://mlsanity.com/paper/2607.21340/) (2026)
- [Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents](https://mlsanity.com/paper/2608.06312/) (2026)
- [On Measuring Semantic Preservation in Legal Ontology Learning](https://mlsanity.com/paper/2608.12326/) (2026)
- [Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?](https://mlsanity.com/paper/2608.21409/) (2026)

## 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.