FactGuard-Bench

FactGuard-Bench is a bilingual long-context benchmark for evaluating and improving whether language models answer only when the supplied document contains sufficient evidence. It contains English and Chinese examples from the book and legal domains, with contexts extending to approximately 128K in the legacy character-based construction buckets.

The benchmark accompanies:

Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard

Dataset summary

The public release contains:

Split	Examples	Answerable	Unanswerable
Train	19,100	6,168	12,932
Validation	1,920	317	1,603
Test	4,200	700	3,500
Total	25,220	7,185	18,035

Task formulation

Every example provides a document and a question. A model should either:

1. answer using evidence from the document, or
2. reject or clarify the question when the document does not support it.

Unanswerable examples cover:

• Lack of Evidence: the answer-bearing evidence is removed;
• Entity Substitution: a supported entity is replaced by a similar but unsupported entity;
• Impossible Condition: the question is augmented with an unsupported constraint.

Entity substitution and impossible-condition insertion form the paper's broader Misleading Evidence category.

Loading

from datasets import load_dataset

dataset = load_dataset("kilizi/FactGuard")

Local loading:

from datasets import load_dataset

dataset = load_dataset(
    "parquet",
    data_files={
        "train": "data/train.parquet",
        "validation": "data/validation.parquet",
        "test": "data/test.parquet",
    },
)

Data fields

Field	Type	Description
id	string	Stable public example identifier within this release
document_id	string	MD5-based identifier inherited from the source document
document	string	Document shown to the evaluated model
question	string	Answerable or adversarial question
response	string	Reference answer or reasoned rejection
answerability	string	answerable or unanswerable
category	string	answerable, lack_of_evidence, or misleading_evidence
perturbation	string	none, evidence_removal, entity_substitution, or impossible_condition
language	string	en or zh
domain	string	book or law
source_file	string	Legacy source/split filename for traceability
legacy_length_bucket	string	Character-based bucket used during construction
document_char_length	int64	Number of Unicode code points in document
question_char_length	int64	Number of Unicode code points in question
response_char_length	int64	Number of Unicode code points in response
evidence	string	Original evidence or source passage, when available
original_question	string	Question before an adversarial transformation
original_answer	string	Answer to the original supported question, when available
unanswerable_reason	string	Specific missing/misaligned evidence explanation
original_entity	string	Entity before substitution
substituted_entity	string	Unsupported replacement entity
unsupported_condition	string	Inserted unsupported condition
question_type	string	Legacy QA-generation type, when available

Empty strings indicate fields that do not apply to a particular perturbation.

Example

example = dataset["test"][0]
prompt = (
    f"Document:\n{example['document']}\n\n"
    f"Please Answer the Question based on the document: {example['question']}"
)

For Chinese examples:

prompt = (
    f"文档:\n{example['document']}\n\n"
    f"请根据文档回答问题: {example['question']}"
)

Construction

FactGuard-Bench was synthesized from long-form English and Chinese book and legal documents. A Qwen2.5-72B-Instruct-based multi-stage workflow generated grounded questions, adversarial transformations, reasoned rejection targets, and automatic quality checks. See the paper and code repository for complete prompts and processing details.

Evaluation

The paper uses a multi-stage LLM-as-a-Judge protocol:

1. detect refusal or clarification;
2. compare answer content with the reference for answerable examples;
3. verify that a correct rejection identifies the actual evidentiary defect.

The test split contains 700 answerable and 3,500 unanswerable examples.

Limitations

• Questions and responses are machine-generated and can contain residual generation or annotation errors.
• The benchmark covers two languages and two primary domains; results should not be treated as representative of every language or application.
• Length buckets used during construction are character based, not tokenizer invariant.
• The dataset is intended to measure document-grounded behavior. It does not establish whether a claim is globally true outside the supplied document.
• The released train/validation/test assignment reproduces the paper's legacy sampling process and is not document-disjoint. Exact overlap statistics are recorded in release_stats.json and printed by the validation script. Preserve these splits when reproducing paper results, but use a newly generated document-disjoint split for claims about generalization to unseen documents.
• Source documents can contain outdated, offensive, or otherwise sensitive material inherited from books and legal corpora.

License and redistribution

FactGuard-Bench is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users may share and adapt the dataset, including for commercial purposes, provided that appropriate attribution is given and modifications are indicated.

Suggested attribution:

FactGuard-Bench, from “Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard,” The FactGuard Authors, 2026.

The dataset repository includes a LICENSE notice. Third-party names, trademarks, and material explicitly identified as third-party content remain subject to their respective rights.

Citation

@article{zhang2026factguard,
  title={Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard},
  author={Zhang, Qian-Wen and Liu, Biao and Li, Fang and Wang, Jie and Qiao, Lingfeng and Yu, Yifei and Yin, Di and Sun, Xing},
  year={2026}
}