Contract Understanding Atticus Dataset (CUAD) Benchmark
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A Contract Understanding Atticus Dataset (CUAD) Benchmark is an expert-annotated specialized legal contract dataset that is a legal NLP benchmark dataset.
- AKA: CUAD, CUAD Benchmark, CUAD Dataset, Contract Understanding Atticus Dataset, CUAD (Commercial Contract Dataset), CUAD (Contract Understanding Atticus Dataset).
- Context:
- It can typically contain CUAD Commercial Legal Contracts with CUAD expert annotations for CUAD contract review tasks.
- It can typically identify CUAD 41 Legal Clause Types crucial for CUAD contract review, especially in CUAD corporate transactions such as CUAD merger and acquisitions.
- It can typically provide CUAD 13,000+ Annotations across CUAD 510 contracts created by CUAD legal experts from The Atticus Project.
- It can typically support CUAD Contract Clause Extraction through CUAD salient portion highlighting.
- It can typically enable CUAD Transformer Model Training through CUAD labeled training data.
- It can typically formulate CUAD Span-Selection Question-Answering Tasks that mirror CUAD SQuAD 2.0 approaches.
- It can typically predict CUAD Start and End Token Positions of CUAD text segments for CUAD legal review.
- It can typically generate CUAD Questions that identify CUAD label categories with CUAD descriptive explanations.
- ...
- It can often facilitate CUAD NLP Research and CUAD NLP development focused on CUAD legal contract review.
- It can often serve as CUAD Challenging Research Benchmark for the broader CUAD NLP community.
- It can often demonstrate CUAD Model Performance Variation based on CUAD model design and CUAD training dataset size.
- It can often require CUAD Expert Annotators for CUAD dataset creation and CUAD quality assurance.
- It can often employ CUAD Five Core Evaluation Metrics designed for CUAD class imbalance handling.
- It can often utilize CUAD Jaccard Similarity Coefficient for CUAD span matching validation.
- It can often address CUAD Needle-in-Haystack Characteristic where CUAD relevant clauses comprise CUAD 10% of contract content.
- It can often apply CUAD Sliding Window Techniques to process CUAD lengthy documents within CUAD transformer limitations.
- ...
- It can range from being a Simple CUAD Clause Detection Task to being a Complex CUAD Contract Understanding Task, depending on its CUAD task complexity.
- It can range from being a Binary CUAD Classification Task to being a Multi-Label CUAD Classification Task, depending on its CUAD label structure.
- It can range from being a Clause-Level CUAD Analysis Task to being a Document-Level CUAD Analysis Task, depending on its CUAD analysis granularity.
- It can range from being a Single-Domain CUAD Application to being a Multi-Domain CUAD Application, depending on its CUAD industry coverage.
- It can range from being a Single-Span CUAD Extraction Task to being a Multi-Span CUAD Recognition Task, depending on its CUAD span complexity.
- ...
- It can support CUAD Contract Review Automation through CUAD machine learning models.
- It can enable CUAD Legal AI Development through CUAD benchmark evaluations.
- It can facilitate CUAD Contract Risk Assessment through CUAD clause identification.
- It can guide CUAD Model Performance Comparison through CUAD standardized metrics.
- It can inform CUAD Legal Technology Advancement through CUAD research findings.
- It can measure CUAD Exact Match (EM) requiring CUAD character-level correspondence.
- It can calculate CUAD F1 Score using CUAD word-level harmonic mean.
- It can compute CUAD Area Under Precision-Recall Curve (AUPR) for CUAD class imbalance handling.
- It can evaluate CUAD Precision at 80% Recall for CUAD practical performance assessment.
- It can assess CUAD Precision at 90% Recall for CUAD stringent evaluation threshold.
- ...
- Example(s):
- CUAD Version Releases, such as:
- CUAD Fine-Tuned Models, such as:
- CUAD RoBERTa-base Model achieving CUAD baseline performance.
- CUAD RoBERTa-large Model demonstrating CUAD improved accuracy.
- CUAD DeBERTa-xlarge Model showing CUAD state-of-the-art results.
- CUAD Clause Label Categorys, such as:
- CUAD Simple Binary Classification Categories (33 types), such as:
- CUAD Agreement Date Clause for CUAD temporal information extraction.
- CUAD Parties Clause for CUAD entity identification.
- CUAD Termination Clause for CUAD contract end conditions.
- CUAD Governing Law Clause for CUAD jurisdiction determination.
- CUAD Confidentiality Clause for CUAD information protection terms.
- CUAD Indemnification Clause for CUAD liability allocation.
- CUAD Limitation of Liability Clause for CUAD damage restrictions.
- CUAD Anti-Assignment Clause for CUAD transfer restriction.
- CUAD Non-Compete Provision for CUAD competition limitation.
- CUAD Complex Entity Extraction Categories (8 types), such as:
- CUAD Effective Date Extraction for CUAD contract start date.
- CUAD Expiration Date Extraction for CUAD contract end date.
- CUAD Renewal Term Extraction for CUAD contract extension period.
- CUAD Liability Cap Extraction for CUAD damage limitation amount.
- CUAD Minimum Commitment Extraction for CUAD obligation threshold.
- CUAD Revenue Percentage Extraction for CUAD financial term.
- CUAD Simple Binary Classification Categories (33 types), such as:
- CUAD Subtask Types, such as:
- CUAD Evaluation Components, such as:
- CUAD Text Normalization Process with CUAD punctuation removal and CUAD lowercase conversion.
- CUAD Word Set Creation through CUAD space tokenization.
- CUAD Jaccard Computation using CUAD set intersection over union.
- CUAD Threshold Application with CUAD 0.5 matching requirement.
- CUAD Confidence-Based Thresholding for CUAD precision-recall curve generation.
- CUAD Downweighting Strategy for CUAD class imbalance mitigation.
- CUAD Multi-Annotator Verification with CUAD three additional expert validators.
- CUAD Performance Categorys, such as:
- CUAD High-Performance Clause Types, such as:
- CUAD Complex Clause Types, such as:
- CUAD Application Domains, such as:
- ...
- Counter-Example(s):
- MAUD (Merger Agreement Dataset), which focuses specifically on MAUD merger agreements rather than CUAD diverse commercial contracts.
- ContractNLI Dataset, which emphasizes ContractNLI natural language inference rather than CUAD clause extraction.
- Legal Judgment Prediction Dataset, which predicts legal case outcomes rather than CUAD contract clause identification.
- Clinical Trial Dataset, which contains medical trial data rather than CUAD legal contract data.
- General NLP Datasets, which lack CUAD legal domain specificity and CUAD expert annotations.
- SQuAD Dataset, which performs general question answering rather than CUAD legal contract analysis.
- See: Legal Contract Review Benchmark Task, Contract Understanding Atticus Dataset (CUAD) Clause Label, Contract Understanding Atticus Dataset (CUAD) Subtask, The Atticus Project, Legal Natural Language Inference (NLI) Task, Legal AI Benchmark, Contract-Focused AI System, Contract Review Fine-Tuned LLM, AI-based Contract Review System, Legal Document Classification Task, Legal Document Corpus, Natural Language Processing, Machine Learning in Legal Tech, Span Selection Question Answering Task, Legal NLP Evaluation Metric.
References
2023
- (Savelka, 2023) ⇒ Jaromir Savelka. (2023). “Unlocking Practical Applications in Legal Domain: Evaluation of Gpt for Zero-shot Semantic Annotation of Legal Texts.” In: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law. DOI:10.1145/3594536.3595161
- QUOTE: ... … selected semantic types from the Contract Understanding Atticus Dataset (CUAD) [16]. Wang et al. assembled and released the Merger Agreement Understanding Dataset (MAUD) [37]. …
2021
- (GitHub, 2021) ⇒ https://github.com/TheAtticusProject/cuad/
- QUOTE: This repository contains code for the Contract Understanding Atticus Dataset (CUAD), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. It is part of the associated paper CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review by Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball.
Contract review is a task about "finding needles in a haystack." We find that Transformer models have nascent performance on CUAD, but that this performance is strongly influenced by model design and training dataset size. Despite some promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
- QUOTE: This repository contains code for the Contract Understanding Atticus Dataset (CUAD), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. It is part of the associated paper CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review by Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball.
2021
- (Hendrycks et al., 2021) ⇒ Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball. (2021). “CUAD: An Expert-annotated Nlp Dataset for Legal Contract Review.” In: arXiv preprint arXiv:2103.06268. doi:10.48550/arXiv.2103.06268
- ABSTRACT: Many specialized domains remain untouched by deep learning, as large labeled datasets require expensive expert annotators. We address this bottleneck within the legal domain by introducing the Contract Understanding Atticus Dataset (CUAD), a new dataset for legal contract review. CUAD was created with dozens of legal experts from The Atticus Project and consists of over 13,000 annotations. The task is to highlight salient portions of a contract that are important for a human to review. We find that Transformer models have nascent performance, but that this performance is strongly influenced by model design and training dataset size. Despite these promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.