CUAD
Models review commercial contracts and highlight the clauses a lawyer would flag, across 41 categories such as governing law, non-compete and change of control. Legal experts from The Atticus Project wrote more than 13,000 labels over 510 contracts. Scores measure how well the extracted spans match the expert labels.
LegalBench reuses many CUAD clause categories as separate tasks. Papers report CUAD with different metrics, such as AUPR and precision at fixed recall, so numbers are not always comparable. About 580 GitHub stars.
- Made by
- The Atticus Project and UC Berkeley
- Released
- March 2021
- Kind
- Law
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Short answer
- Tasks
- All public
In the authors’ words
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.
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for CUAD yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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