Datasets:

orgrctera
/

legalbenchrag_cuad_qa

language:
  - en
license: cc-by-4.0
tags:
  - legal
  - question-answering
  - rag
  - contracts
  - cuad
pretty_name: LegalBenchRAG — CUAD (QA)
size_categories:
  - 1K<n<10K
task_categories:
  - question-answering

LegalBenchRAG: CUAD (question answering)

This dataset is the CUAD slice of LegalBench-RAG, packaged as a question answering (QA) task over commercial contract text. Each row pairs a natural-language query about a named agreement with one or more gold answer strings: verbatim (or minimally normalized) spans from the underlying contract that answer the question. This mirrors how practitioners review contracts—locating the clauses that substantiate a yes/no or fact-seeking question—while staying aligned with the LegalBench-RAG benchmark family’s focus on legally grounded supervision.

Background: CUAD (Contract Understanding Atticus Dataset)

CUAD (CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review, Hendrycks et al., NeurIPS 2021 Datasets and Benchmarks Track) is a large, expert-annotated resource for legal contract review. It was built with legal experts from The Atticus Project and comprises 510 PDF commercial contracts and over 13,000 labels across 41 clause categories relevant to transactions such as M&A (e.g. termination, liability caps, assignment, confidentiality, IP, and payment terms).

The original CUAD modeling task is to identify salient spans in a contract that a lawyer should examine for a given clause type—i.e. highlighting important text for human review. Transformer baselines show promising but far-from-saturated performance, and CUAD remains a standard benchmark for long-document legal NLP.

Original resources: The Atticus Project — CUAD · GitHub: TheAtticusProject/cuad
License: CC BY 4.0 (see the official CUAD release for full terms).

Task: question answering for CUAD (LegalBenchRAG QA)

In this Hugging Face distribution, the task is question answering on the CUAD corpus under the LegalBench-RAG benchmark: given an input query that names an agreement and asks a concrete legal question (often phrased in a yes/no style), a system should produce answer text that is supported by the contract—here represented as a JSON-encoded list of strings, each string being a gold answer span (one or more disjoint clauses may be required for a single question).

Typical uses include:

Training or evaluating extractive QA and long-context models on real contracts.
RAG pipelines where the retriever must surface passages that a generator can quote as answers.
Benchmarking legal LMs against human-annotated reference strings rather than only automatic metrics on generic QA.

This slice is explicitly labeled qa in the LegalBenchRAG export (as opposed to span-offset retrieval formulations used for other sub-benchmarks).

Relation to LegalBench and LegalBench-RAG

LegalBench aggregates many tasks for evaluating legal reasoning in language models.
LegalBench-RAG (Pipitone & Alami, 2024) evaluates the retrieval component of legal RAG with thousands of query–answer pairs and minimal relevant segments annotated by legal experts across multiple corpora, including CUAD, ContractNLI, MAUD, and PrivacyQA. Public artifacts are linked from the paper and community repositories (e.g. zeroentropy-ai/legalbenchrag).

When citing work that uses this dataset, cite CUAD for the underlying contracts and annotations, LegalBench-RAG for the benchmark framing, and this dataset card for this QA packaging.

Dataset structure

Field	Description
`id`	Unique example identifier (UUID string).
`input`	Natural-language query, typically opening with “Consider the …” and naming parties and the agreement, followed by a yes/no-style or factual legal question (e.g. anti-assignment, liability caps).
`expected_output`	JSON string encoding a list of strings. Each string is a gold answer: contract language that answers the question (multiple strings when several clauses jointly support the answer).
`metadata.item`	JSON string with auxiliary export fields (e.g. duplicated query and answer list for traceability).

The default split contains 4,042 rows.

Example records

Example 1 — single gold span (expected_output is a string containing JSON):

input:
Consider the First Amendment to Distributor Agreement between Peregrine/Bridge Transfer Corporation, NEON Systems, Inc., and Skunkware, Inc.; Is there an anti-assignment clause in this contract?

expected_output (parsed):

[
  "Any sale,           transfer or other conveyance of all or any part of the stock in, or           assets of, Licensor in violation of this Section shall be null and           void."
]

Example 2 — multiple gold spans for one query (excerpt; liability-related question with several supporting passages):

input:
Consider the Global Master Supply Agreement between ExxonMobil Chemical Company and West Pharmaceutical Services, Inc.; Is there a cap on liability under this contract?

expected_output (parsed; first two strings shown; the full list contains five gold strings in the dataset):

[
  "In no event shall either party be responsible for any special, punitive, or consequential damages whatsoever.",
  "All claims for any cause whatsoever, whether based in contract, negligence or other tort, strict liability, breach of warranty or otherwise, shall be deemed waived unconditionally and absolutely unless Seller receives written notice of such claim not later than one hundred fifty (150) days after Buyer's receipt of Product as to which such claim is made. …"
]

Evaluators typically parse expected_output and compare model outputs using token- or span-level overlap, exact match on normalized text, or LLM-as-judge protocols—consistent with the LegalBench-RAG emphasis on faithful quoting from source contracts.

References

CUAD (original dataset and contract-review task)
Dan Hendrycks, Collin Burns, Anya Chen, Spencer Ball. CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review. NeurIPS 2021 Datasets and Benchmarks Track.
NeurIPS Proceedings · arXiv:2103.06268 · PDF

Abstract (short): Expert-annotated contract review dataset with 13,000+ labels over 510 agreements; task is to highlight text important for review; Transformer models show nascent performance with large headroom for improvement.
```
@inproceedings{hendrycks2021cuad,
  title = {{CUAD}: An Expert-Annotated {NLP} Dataset for Legal Contract Review},
  author = {Hendrycks, Dan and Burns, Collin and Chen, Anya and Ball, Spencer},
  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
  year = {2021},
  url = {https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/6ea9ab1baa0efb9e19094440c317e21b-Abstract.html}
}
```
LegalBench-RAG (retrieval benchmark; includes CUAD among sources)
Nicholas Pipitone and Ghita Houir Alami. LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain. arXiv:2408.10343, 2024.
arXiv · PDF

Abstract (short): Proposes evaluating retrieval for legal RAG with minimal human-annotated segments so metrics can be precise and generations can cite sources without huge irrelevant chunks.
```
@article{pipitone2024legalbenchrag,
  title = {{LegalBench-RAG}: A Benchmark for Retrieval-Augmented Generation in the Legal Domain},
  author = {Pipitone, Nicholas and Alami, Ghita Houir},
  journal = {arXiv preprint arXiv:2408.10343},
  year = {2024}
}
```
LegalBench (umbrella)
LegalBench — broader suite of legal NLP tasks.

Acknowledgments

Data lineage traces to CUAD (The Atticus Project) and the LegalBench-RAG benchmark construction. Use of the original CUAD materials remains subject to CC BY 4.0 and any additional terms on the official CUAD release. This Hugging Face export was produced from a Langfuse-tracked export (metadata.item may mirror query/answer fields for provenance).