| import json |
| import datasets |
| from pathlib import Path |
|
|
|
|
| _DESCRIPTION = "OpenExempt is a diagnostic benchmark for legal reasoning in language models." |
| _HOMEPAGE = "https://github.com/servantez/OpenExempt" |
| _LICENSE = "CC BY 4.0" |
| _VERSION = datasets.Version("1.0.0") |
| _CITATION = """ |
| @misc{servantez2026openexemptdiagnosticbenchmarklegal, |
| title={OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand}, |
| author={Sergio Servantez and Sarah B. Lawsky and Rajiv Jain and Daniel W. Linna Jr. and Kristian Hammond}, |
| year={2026}, |
| eprint={2601.13183}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2601.13183}, |
| } |
| """ |
|
|
| _SUITES = { |
| "advanced_competency": { |
| "archive": "data/advanced_competency.tar.gz", |
| "description": "Advanced Competency Suite." |
| }, |
| "basic_competency": { |
| "archive": "data/basic_competency.tar.gz", |
| "description": "Basic Competency Suite." |
| }, |
| "intermediate_competency": { |
| "archive": "data/intermediate_competency.tar.gz", |
| "description": "Intermediate Competency Suite." |
| }, |
| "asset_scaling": { |
| "archive": "data/asset_scaling.tar.gz", |
| "description": "Asset Scaling Suite." |
| }, |
| "temporal_reasoning": { |
| "archive": "data/temporal_reasoning.tar.gz", |
| "description": "Temporal Reasoning Suite." |
| }, |
| "reasoning_decomposition": { |
| "archive": "data/reasoning_decomposition.tar.gz", |
| "description": "Reasoning Decomposition Suite." |
| }, |
| "baseline_robustness": { |
| "archive": "data/baseline_robustness.tar.gz", |
| "description": "Baseline Robustness Suite." |
| }, |
| "distractor_robustness": { |
| "archive": "data/distractor_robustness.tar.gz", |
| "description": "Distractor Robustness Suite." |
| }, |
| "obfuscation_robustness": { |
| "archive": "data/obfuscation_robustness.tar.gz", |
| "description": "Obfuscation Robustness Suite." |
| }, |
| "sycophancy_robustness": { |
| "archive": "data/sycophancy_robustness.tar.gz", |
| "description": "Sycophancy Robustness Suite." |
| }, |
| } |
|
|
| def read_json(path: Path): |
| with path.open("r", encoding="utf-8") as file: |
| return json.load(file) |
|
|
| def read_jsonl_file(path: Path): |
| with path.open("r", encoding="utf-8") as file: |
| return [json.loads(line) for line in file if line.strip()] |
|
|
| class OpenExemptConfig(datasets.BuilderConfig): |
|
|
| def __init__(self, suite, **kwargs): |
| if suite == "all": |
| description = _DESCRIPTION |
| archives = [info["archive"] for info in _SUITES.values()] |
| else: |
| info = _SUITES[suite] |
| description = f"OpenExempt: {info['description']}" |
| archives = [info["archive"]] |
| super(OpenExemptConfig, self).__init__( |
| name=suite, |
| description=description, |
| version=_VERSION, |
| **kwargs) |
| self.suite = suite |
| self.archives = archives |
| |
| class OpenExempt(datasets.GeneratorBasedBuilder): |
| BUILDER_CONFIG_CLASS = OpenExemptConfig |
| BUILDER_CONFIGS = [ |
| OpenExemptConfig(suite=suite) |
| for suite in ["all"] + list(_SUITES.keys()) |
| ] |
| DEFAULT_CONFIG_NAME = "all" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| features=datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "prompt": datasets.Value("string"), |
| "solution": datasets.Value("string"), |
| "config": datasets.Value("string"), |
| "case": datasets.Value("string") |
| } |
| ), |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| extracted_paths = dl_manager.download_and_extract(self.config.archives) |
| dataset_dirs = [] |
| for extracted_path in extracted_paths: |
| suite_dir = next(Path(extracted_path).iterdir()) |
| for dataset_dir in suite_dir.iterdir(): |
| if dataset_dir.is_dir(): |
| dataset_dirs.append(str(dataset_dir)) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "dataset_dirs": dataset_dirs, |
| "split": "dev", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "dataset_dirs": dataset_dirs, |
| "split": "test", |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, dataset_dirs, split): |
| for dataset_dir in dataset_dirs: |
| dataset_dir = Path(dataset_dir) |
|
|
| config = read_json(dataset_dir / "config.json") |
| shared = read_json(dataset_dir / "shared.json") |
| examples = read_jsonl_file(dataset_dir / f"{split}.jsonl") |
| case_file_name = 'cases' if split == 'test' else f'{split}_cases' |
| cases = read_jsonl_file(dataset_dir / f'{case_file_name}.jsonl') |
|
|
| if len(examples) != len(cases): |
| raise ValueError(f"Number of examples and cases do not match for dataset: {dataset_dir}") |
| for example, case in zip(examples, cases): |
| uid = example["uid"] |
| prompt_inputs = [ |
| shared["instruction"], |
| shared["meta_instruction"], |
| shared["response_format"], |
| example["facts"], |
| example.get("solved_steps"), |
| shared["statutes"], |
| shared["format_reminder"] |
| ] |
| prompt = '\n\n'.join(filter(None, prompt_inputs)) |
| solution = example["solution"] |
| if isinstance(solution, dict): |
| solution = json.dumps(solution, sort_keys=True) |
| |
| yield uid, { |
| "id": uid, |
| "prompt": prompt, |
| "solution": solution, |
| "config": json.dumps(config, sort_keys=True), |
| "case": json.dumps(case, sort_keys=True) |
| } |
