README.md

---
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.

## 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).

## 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).