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README.md

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+---
+language:
+- en
+- zh
+license: cc-by-4.0
+task_categories:
+- text-generation
+- question-answering
+tags:
+- hallucination
+- regulatory-compliance
+- preference-optimization
+- dpo
+- long-context
+- detail-faithfulness
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.jsonl
+  - split: validation
+    path: data/val-*.jsonl
+  - split: test
+    path: data/test-*.jsonl
+---
+
+# DetailBench
+
+**A Benchmark for Detail Hallucination in Long Regulatory Documents**
+
+DetailBench is a benchmark for evaluating and mitigating *detail hallucination* in LLM outputs on long regulatory documents. It accompanies the paper:
+
+> **DetailDPO: Targeted Preference Optimization for Reducing Detail Hallucination in Long Regulatory Documents**
+> (EMNLP 2026)
+
+## Overview
+
+Large language models frequently produce *detail hallucinations*—subtle errors in threshold values, units, scopes, obligation levels, and conditions—when processing long regulatory documents. DetailBench provides:
+
+- **322 source documents** (172 real + 150 synthetic) from three jurisdictions
+- **13,000 preference pairs** (10,000 train / 1,000 validation / 2,000 test)
+- **Five detail error types** (τ₁–τ₅) with balanced training distribution
+- **Three context-length tiers**: Short (8K–16K), Medium (16K–32K), Long (32K–64K tokens)
+
+## Data Sources
+
+| Source | Count | Description |
+|--------|------:|-------------|
+| GB Standards | 65 | Chinese national standards on hydrogen production, storage, transportation, and safety |
+| US CFR | 31 | Code of Federal Regulations (Title 49: Transportation, Title 40: Environmental Protection) via eCFR API |
+| EUR-Lex | 76 | EU regulations on hydrogen infrastructure, clean energy, pressure equipment via CELLAR API |
+| Synthetic | 150 | Domain-template generated documents for training augmentation |
+
+## Schema
+
+Each sample in the JSONL files contains:
+
+```json
+{
+  "sample_id": "test_00000",
+  "context_tier": "long",
+  "token_count": 43368,
+  "documents": [
+    {
+      "doc_id": "SYNTH_0075",
+      "source": "synthetic",
+      "segments": [
+        {
+          "segment_id": "SYNTH_0075_seg_0",
+          "text": "...",
+          "token_count": 605
+        }
+      ]
+    }
+  ],
+  "query": "An electrolyser plant produces hydrogen at ...",
+  "chosen": {
+    "is_compliant": true,
+    "constraints": [
+      {"type": "tau_1", "description": "...", "value": "82", "unit": "°C"}
+    ],
+    "evidence": [
+      {"segment_id": "...", "quote": "..."}
+    ]
+  },
+  "rejected": {
+    "is_compliant": true,
+    "constraints": ["... (with one perturbed detail)"],
+    "evidence": ["..."]
+  },
+  "perturbation": {
+    "error_type": "tau_5_condition",
+    "original_value": "where appropriate",
+    "perturbed_value": "[dropped]",
+    "detail_element_id": "...",
+    "segment_id": "..."
+  },
+  "detail_elements": [
+    {
+      "element_id": "...",
+      "type": "tau_1",
+      "value": "3928.0",
+      "unit": "kg",
+      "span": [46, 55],
+      "segment_id": "...",
+      "quote": "..."
+    }
+  ]
+}
+```
+
+## Detail Error Taxonomy
+
+| Type | Name | Description | Example |
+|------|------|-------------|---------|
+| τ₁ | Threshold | Numeric value errors | "pressure ≤ **35** MPa" → "pressure ≤ **45** MPa" |
+| τ₂ | Unit | Measurement unit errors | "distance in **meters**" → "distance in **feet**" |
+| τ₃ | Scope | Applicability scope errors | "for **indoor** facilities" → "for **all** facilities" |
+| τ₄ | Level | Obligation level errors | "**shall** comply" → "**should** comply" |
+| τ₅ | Condition | Conditional clause errors | "if temperature **exceeds 60°C**" → condition dropped |
+
+## Evaluation Metrics
+
+- **Compliance Accuracy**: Fraction of correct compliance judgments
+- **Detail Error Rate (DER)**: Per-type and overall error rate on detail elements
+- **Evidence F1**: Precision/recall/F1 of predicted evidence citations
+- **Evidence Consistency**: Fraction of citations where quoted text matches source
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("YOUR_USERNAME/DetailBench")
+
+# Access splits
+train = ds["train"]      # 10,000 samples
+val = ds["validation"]   # 1,000 samples
+test = ds["test"]        # 2,000 samples
+
+# Example: inspect a test sample
+sample = test[0]
+print(sample["query"])
+print(sample["context_tier"])  # "short", "medium", or "long"
+print(len(sample["documents"]))
+```
+
+## Split Statistics
+
+| Split | Samples | Short | Medium | Long |
+|-------|--------:|------:|-------:|-----:|
+| Train | 10,000 | 6,463 | 2,263 | 1,274 |
+| Val | 1,000 | 605 | 249 | 146 |
+| Test | 2,000 | 1,215 | 519 | 266 |
+
+Error type distribution in the training set is balanced at 20% each (2,000 per type).
+
+## Citation
+
+```bibtex
+@inproceedings{detaildpo2026,
+  title={DetailDPO: Targeted Preference Optimization for Reducing Detail Hallucination in Long Regulatory Documents},
+  author={...},
+  booktitle={Proceedings of EMNLP 2026},
+  year={2026}
+}
+```
+
+## License
+
+This dataset is released under CC-BY-4.0. The underlying regulatory documents are sourced from public government repositories (eCFR, EUR-Lex, openstd.samr.gov.cn).

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