LegalBench.BR ⚖️🇧🇷

LegalBench.BR is a benchmark for evaluating large language models (LLMs) on tasks grounded in Brazilian Law and written in Brazilian Portuguese.

The dataset was designed to test whether general-purpose and legal-domain LLMs can answer, classify, infer, and recall legal information in the context of the Brazilian legal system. It covers multiple legal areas and combines synthetic legal questions, court-decision excerpts, legal entailment tasks, and closed-book rule-recall prompts.

Goal: provide a reproducible evaluation suite for Brazilian legal NLP and for open legal language models trained or adapted to Brazilian Law.

Dataset repository: https://huggingface.co/datasets/celsowm/legalbench.br

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Why LegalBench.BR?

Most legal benchmarks for LLMs are centered on foreign jurisdictions, especially common-law systems, or on legal tasks in English. Brazilian Law has its own legal sources, terminology, procedures, institutions, and exam formats. A model that performs well on general legal benchmarks may still fail when asked to reason about Brazilian statutes, legal areas, bar-exam-style questions, or court excerpts.

LegalBench.BR was created to help evaluate models under conditions closer to Brazilian legal practice, including:

• multiple-choice legal questions;
• classification of court excerpts by legal area;
• binary legal entailment over factual-legal scenarios;
• closed-book recall of legal provisions;
• Brazilian Portuguese legal language;
• tasks inspired by statutes, case law, legal education, and practical legal reasoning.

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Dataset Summary

LegalBench.BR contains 1,000 prompts distributed across 15 areas of Brazilian Law and 4 task families.

Task family	Type value	Instances	Main skill evaluated
Multiple-choice legal QA	multiple_choice_qa	443	Legal knowledge and application to cases
Legal-area classification	text_classification	285	Classification of court excerpts into legal areas
Binary legal entailment	binary_entailment	200	Whether a legal conclusion follows from a factual-legal statement
Closed-book rule recall	closed_book_qa	72	Recall of the exact legal provision/article from its text

The benchmark is intended for evaluation, not model training.

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Legal Areas

The dataset covers the following areas:

• Direito Administrativo
• Direito Ambiental
• Direito Civil
• Direito Constitucional
• Direito Constitucional Penal
• Direito Eleitoral
• Direito Empresarial
• Direito Internacional
• Direito Penal
• Direito Previdenciário
• Direito Processual Civil
• Direito Processual Penal
• Direito Trabalhista / Direito do Trabalho
• Direito Tributário
• Ética Profissional

Minor label variants may appear in the dataset, such as Direito Trabalhista and Direito do Trabalho.

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Task Families

1. Multiple-Choice Legal Question Answering (multiple_choice_qa)

The model receives a legal question and several alternatives, only one of which is correct. This task evaluates whether the model can identify concepts, principles, legal instruments, and solutions to short Brazilian legal problems.

Example:

Question:
Qual princípio constitucional está relacionado à proibição do retrocesso social?

Alternatives:
a) Princípio da razoabilidade
b) Princípio da dignidade da pessoa humana
c) Princípio da vedação ao retrocesso
d) Princípio da eficiência

Answer:
c

This task includes both conceptual questions and applied case-style questions.

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2. Legal-Area Classification (text_classification)

The model receives a short excerpt from a Brazilian court decision and must classify it into one legal area.

The excerpts were collected from decisions of Brazilian superior courts, especially STF and STJ, when the source material contained a single official legal-area label. Multi-label decisions were excluded to preserve a single-label classification setup.

Example:

Question:
A qual área do direito brasileiro pertence esta jurisprudência do STJ?

Excerpt:
RECURSO EM MANDADO DE SEGURANÇA. ICMS-DIFAL. ILEGITIMIDADE PASSIVA DA AUTORIDADE COATORA...

Alternatives:
a) Direito Administrativo
b) Direito Civil
c) Direito Constitucional
...
j) Direito Tributário

Answer:
Direito Tributário

Before inclusion, labels that would reveal the answer inside the excerpt were removed from the prompt text.

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3. Binary Legal Entailment (binary_entailment)

The model receives a short factual-legal scenario and a legal hypothesis. It must decide whether the hypothesis is true or false in light of the scenario.

Example:

Area:
Direito Civil

Statement:
Lucas, com 16 anos e emancipado pelos pais, celebrou contrato de locação residencial sem a presença ou autorização de seus pais ou responsáveis legais.

Hypothesis:
O contrato de locação residencial celebrado por Lucas é válido e plenamente eficaz, considerando sua condição de emancipado.

Task:
Esta hipótese é verdadeira ou falsa?

Answer:
verdadeira

This task evaluates legal inference and the ability to apply rules to concrete facts.

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4. Closed-Book Rule Recall (closed_book_qa)

The model receives the text of a legal provision and must identify the corresponding legal article or reference without retrieval.

Example:

Question:
Qual é o artigo da Constituição Brasileira de 1988 com o texto:
"É vedado aos Estados, ao Distrito Federal e aos Municípios estabelecer diferença tributária entre bens e serviços, de qualquer natureza, em razão de sua procedência ou destino."?

Answer:
Art. 152, CF/88

This task evaluates memorized legal knowledge and susceptibility to hallucinated legal citations.

The closed-book subset covers provisions from:

Legal source	Abbreviation
Constituição da República Federativa do Brasil de 1988	CF/88
Código Civil, Lei 10.406/2002	CC
Código Penal, Decreto-Lei 2.848/1940	CP
Código de Processo Civil, Lei 13.105/2015	CPC
Código Tributário Nacional, Lei 5.172/1966	CTN
Consolidação das Leis do Trabalho, Decreto-Lei 5.452/1943	CLT
Código Florestal, Lei 12.651/2012	CFlo
Lei de Licitações e Contratos Administrativos, Lei 14.133/2021	NLLCA

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Dataset Structure

Each example contains the following fields:

Field	Description
id	Unique example identifier
type	Task type: multiple_choice_qa, text_classification, binary_entailment, or closed_book_qa
legal_area	Area of Brazilian Law associated with the example
messages	Chat-completion formatted messages, usually with system and user roles
answer	Gold answer: alternative letter, class label, boolean string, or legal article reference

Example schema:

{
  "id": "civil_001",
  "type": "binary_entailment",
  "legal_area": "Direito Civil",
  "messages": [
    {
      "role": "system",
      "content": "Você é um avaliador jurídico especializado em Direito Brasileiro."
    },
    {
      "role": "user",
      "content": "..."
    }
  ],
  "answer": "verdadeira"
}

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Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("celsowm/legalbench.br", split="train")
print(dataset[0])

Using pandas:

from datasets import load_dataset

df = load_dataset("celsowm/legalbench.br", split="train").to_pandas()
print(df.head())

Filtering by task:

mcqa = dataset.filter(lambda x: x["type"] == "multiple_choice_qa")
entailment = dataset.filter(lambda x: x["type"] == "binary_entailment")
classification = dataset.filter(lambda x: x["type"] == "text_classification")
closed_book = dataset.filter(lambda x: x["type"] == "closed_book_qa")

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Suggested Evaluation Protocol

LegalBench.BR is designed for deterministic or low-temperature evaluation.

Recommended generation settings:

temperature = 0.0
top_p = 1.0
max_new_tokens = 128

For multiple-choice tasks, models should output only the selected alternative, such as:

c

For binary entailment tasks, models should output:

verdadeira

or

falsa

For legal-area classification tasks, models should output the target legal area.

For closed-book rule-recall tasks, models should output the article reference, such as:

Art. 152, CF/88

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Metrics

Recommended metrics by task:

Task	Primary metric	Optional metrics
multiple_choice_qa	Accuracy	Exact match, normalized accuracy
text_classification	Accuracy	Macro-F1, per-area F1
binary_entailment	Accuracy	Precision, recall, F1
closed_book_qa	Exact Match	Normalized exact match, citation error rate

For model papers, we recommend reporting:

• overall score;
• score by task family;
• score by legal area;
• error analysis for hallucinated or unsupported legal references;
• comparison against the base model when evaluating a fine-tuned model.

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Data Creation and Review

LegalBench.BR combines synthetic legal tasks and excerpts from Brazilian legal material.

The synthetic questions were designed to evaluate legal reasoning, conceptual understanding, and rule application in Brazilian Law. The court-excerpt classification subset uses real judicial text excerpts, with answer-leaking labels removed from the prompt.

Synthetic instances underwent legal review to verify:

• correctness of the legal answer;
• clarity of the statement or question;
• plausibility of alternatives and distractors;
• consistency with Brazilian legal terminology;
• absence of misleading or unsupported answer keys.

During review, approximately 15% of the synthetic questions required correction, such as answer-key fixes, terminology adjustments, or rewriting for greater legal fidelity.

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Intended Uses

LegalBench.BR is intended for:

• evaluating LLMs on Brazilian legal tasks;
• comparing base and fine-tuned legal language models;
• measuring legal-area classification performance;
• testing rule recall and citation accuracy;
• studying hallucination risks in Brazilian legal contexts;
• benchmarking open and proprietary models under a common protocol.

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Out-of-Scope Uses

LegalBench.BR should not be used as:

• a substitute for professional legal advice;
• a source of authoritative legal interpretation;
• a training set for models that will later be evaluated on the same benchmark without proper disclosure;
• a basis for automated legal decision-making affecting rights or obligations.

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Limitations

• Some benchmark instances are synthetic and may not capture the full complexity of real legal practice.
• LegalBench.BR is not a complete representation of Brazilian Law.
• Closed-book rule recall evaluates memorized legal references and does not replace retrieval-based legal verification.
• The dataset may contain label variants and residual imperfections.
• Legal norms may change over time; models evaluated on this benchmark should still be checked against current official sources.
• The benchmark does not measure all dimensions of legal competence, such as drafting long legal briefs, strategic litigation, or full procedural reasoning.

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Relationship to Open-Advogado

LegalBench.BR can be used as an evaluation benchmark for Brazilian legal language models, including models trained with the Open-Advogado pipeline and the Legal BR SFT dataset.

Related resources:

• LegalBench.BR: https://huggingface.co/datasets/celsowm/legalbench.br
• Legal BR SFT: https://huggingface.co/datasets/celsowm/legal_br_sft
• Open-Advogado code: https://github.com/celsowm/open-advogado
• qwen3.5-4b-legal-br: https://huggingface.co/celsowm/qwen3.5-4b-legal-br

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Citation

If you use LegalBench.BR, please cite the dataset and related paper when available.

@misc{fontes2026legalbenchbr,
  title        = {LegalBench.BR: A Benchmark for Evaluating Language Models in Brazilian Law},
  author       = {Fontes, Celso},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/celsowm/legalbench.br}},
  note         = {Dataset for Brazilian legal NLP evaluation}
}

Suggested citation text:

Fontes, C. (2026). LegalBench.BR: A Benchmark for Evaluating Language Models in Brazilian Law. Hugging Face Dataset. https://huggingface.co/datasets/celsowm/legalbench.br

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License

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

See: https://creativecommons.org/licenses/by-sa/4.0/

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Disclaimer

LegalBench.BR is a research benchmark. It does not provide legal advice and should not be used as the sole basis for legal decisions. Outputs produced by models evaluated with this benchmark must be independently verified by qualified legal professionals before practical use.