MultiHoax: A Dataset of Multi-hop False-Premise Questions

Hamidreza Saffari; Mohammadamin Shafiei; Nafise Sadat Moosavi

REVIEW 3 major objections 7 minor 19 references

MultiHoax: A Dataset of Multi-hop False-Premise Questions

T0 review · 3 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Even the strongest tested large language model detects the false premise in only 26% of MultiHoax's multi-hop questions.

desk verdict A genuinely useful new benchmark at the intersection of multi-hop reasoning and false premises, but the ground truth has a verification gap that needs closing before the numbers can be trusted. read the letter →

arxiv 2506.00264 v2 pith:Y4DHJPMO submitted 2025-05-30 cs.CL

Mohammadamin Shafiei , Hamidreza Saffari , Nafise Sadat Moosavi This is my paper

classification cs.CL

keywords multi-hopreasoningfalse-premisequestionsquestionansweringlargelanguagemodelsbenchmarkdatasetWikipediagroundingIdonotknow

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

MultiHoax is a new benchmark of 700 multiple-choice questions where the only correct answer is 'I do not know' because at least one hidden factual assumption in the question is false. Unlike earlier false-premise datasets, the falsehood can only be uncovered after two or more reasoning steps, and the questions span seven countries and ten knowledge domains with Wikipedia as the factual ground truth. In the paper's two-stage evaluation, a model must both choose 'I do not know' and explain the false premise to score; the best model does this on 26% of questions, and open-source models fall to near zero. The paper argues that multi-hop false premises are a distinct, currently unresolved failure mode of large language models.

What carries the argument

The central object is the multi-hop false-premise question (MHFPQ): a multiple-choice item whose question text contains a false assumption that requires at least two inference steps to reveal, paired with three plausible distractors and an 'I do not know' option that is the sole correct answer. The benchmark's machinery includes a taxonomy of false-premise types (event, property, scope, entity), a taxonomy of multi-hop reasoning types (named-entity, temporal, geographical, intersection, comparison), and a two-stage evaluation protocol in which a model must first reject the question and then justify the rejection by naming the false premise, so that uncertainty is not counted as false-premise detection.

What would settle it

Run the same 700 questions through a panel of, say, five knowledgeable human annotators under the paper's two-stage protocol, and independently audit every question against its linked Wikipedia page. If humans frequently disagree about the correct answer, or if an audit finds a substantial fraction of questions whose 'false' assumption is actually true or unanswerable rather than false, then the assumption that 'I do not know' is uniquely correct does not hold and the measured model failure rates lose their meaning.

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Extended reading notes

Core claim

The authors present MultiHoax as the first false-premise question resource that combines multi-hop reasoning with false-premise detection across multiple countries and knowledge domains. Each of its 700 questions embeds a globally false factual assumption that becomes apparent only after connecting two or more pieces of information, and the correct answer is always 'I do not know' rather than any of three plausible distractors. Using a two-stage protocol that separates choosing 'I do not know' from justifying that choice, the authors find that the strongest tested model correctly detects the false premise 26% of the time, and that performance remains low across every country, knowledge category, false-premise type, and multi-hop reasoning type tested.

Load-bearing premise

The load-bearing premise is that every question's embedded 'false premise' is genuinely false worldwide and verifiable through Wikipedia, making 'I do not know' the only correct answer.

Editorial extensions

If this is right

Single-hop false-premise benchmarks overstate current models' abilities; adding a second reasoning step drops the best two-stage detection rate to 26%.
The two-stage protocol should become standard for false-premise evaluation, since refusing to answer alone conflates detection with lack of knowledge.
Models that pass this benchmark will need to verify intermediate facts against each other, not just retrieve a final answer.
Open-source models are far behind proprietary ones on this task, with justification accuracy near 1-6%, so the benchmark offers a concrete target for open-source reasoning improvements.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural stress test is retrieval-augmented or agentic systems: if the base model cannot reject a false premise, adding retrieved evidence may increase its confidence in the wrong answer rather than correct it.
The gap between first-stage refusal rates (up to 46%) and second-stage justification rates (up to 26%) suggests that many 'I do not know' responses are hedges, not detections; explicit confidence ratings could test this directly.
Because the dataset is English-only and Wikipedia-based, measured performance reflects English-Wikipedia reasoning; translating questions into local languages or grounding them in local sources could change results substantially.
The authors note lower accuracy for U.S. questions, which they attribute to the generator producing harder questions about better-known facts; human-rated difficulty per country would let future work separate model knowledge gaps from question difficulty.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

MultiHoax is worth knowing about: it's the first false-premise QA benchmark that combines multi-hop reasoning with cross-country, cross-category coverage. The construction pipeline is described in unusual detail — fact extraction from Wikipedia, two distinct generation prompts, a two-round human review with explicit selection criteria, and a secondary verification pass with three possible labels. That transparency is genuine credit. The empirical finding — that even the best models detect the false premise under the two-stage protocol only about a quarter of the time — is consistent with what we know about LLM sycophancy and is likely to replicate in broad strokes even if the exact numbers shift.

The soft spots are real. The second review round flagged 124 of 700 questions as either 'no false information' (67) or 'cannot tell' (57). Those were modified and given feedback, but there's no third-round independent check that the fixes actually made the falsehood global and verifiable. Since every model score is computed against the assumption that 'I do not know' is the unique correct answer, an unverified 18% of the dataset is a load-bearing gap, not a cosmetic one. The authors should re-release the dataset with a final human pass on the modified items, report inter-annotator agreement on at least a sample, and give human accuracy on the final set. Without a human baseline, we can't tell whether the 23% reflects model weakness or ambiguous items.

The Table 3 inconsistency (GPT-4o's task-2 score of 0.25 exceeding its task-1 score of 0.23, when task-2 is conditioned on task-1) is almost certainly a presentation error — the numbers in Tables 4–6 and the individual model patterns suggest the task-1 value might be a typo — but it should be fixed before publication. The generator-evaluator overlap (Claude generated the questions and scores highest) is a bias to disclose, not a fatal flaw; the conclusion that open models do worse is robust across categories.

Who is this for? Anyone building or evaluating LLMs for factuality and reasoning robustness. It deserves a serious referee: the resource fills a real gap, and the flaws are fixable. I'd send it out, with the expectation that the authors add the missing verification and human baseline.

Referee Report

3 major / 7 minor

Summary. The paper introduces MultiHoax, a benchmark of 700 multi-hop false-premise questions (MHFPQs) spanning seven countries and ten knowledge categories, with Wikipedia grounding. The construction pipeline uses Claude 3.5 Haiku to extract facts from Wikipedia and generate questions, followed by an expert curation step and a second review round by three annotators. The authors evaluate six LLMs with a two-stage protocol: a multiple-choice QA that includes an 'I do not know' option, and a justification prompt for models that choose that option. The reported results show low performance across models, with the best model (Claude 3.5 Sonnet) selecting 'I do not know' on 46% of questions and correctly justifying 23% of those refusals. The paper argues that this demonstrates a significant gap in multi-hop false-premise reasoning.

Significance. If the dataset's ground truth is reliable, MultiHoax addresses a real gap by combining multi-hop reasoning with implicit false premises, and its cross-country and cross-category coverage is a useful extension of existing FPQ benchmarks. The authors release the dataset and provide prompts and annotation guidelines in the appendix, which aids reproducibility. However, the empirical contribution is currently weakened by insufficient verification of the final dataset, ambiguous evaluation metrics, and the overlap between the generative and evaluated model families. The resource has clear potential value once these issues are resolved.

major comments (3)

[3.5, Table 19] The second-round verification confirmed only 576 of 700 questions as containing false information; 57 were labeled 'I cannot tell' and 67 'There is no false information'. The manuscript states that these 124 items were modified (falsehood added or clarity improved) and feedback was provided, but no independent third-round re-verification of the modified items is reported. Because every model score in Tables 3-6 and 20-21 is computed against the assumption that 'I do not know' is the only correct answer for every question, any remaining item whose falsehood is not established would directly affect the headline accuracies and all breakdowns. The authors should provide a final human verification of the complete 700-question set, report inter-annotator agreement (e.g., Cohen's kappa or Fleiss' kappa) for the review rounds, and report human performance on a sample. Without this, the central claim that every question is a valid false-premise question is not empirically supported.
[4, Table 3] The two task metrics are not defined with explicit denominators. The text describes Task 2 as applying only to models that selected 'I do not know' in Task 1, but the table reports it as a raw accuracy without a note. This creates an apparent, though not real, inconsistency (e.g., GPT-4o's Task-2 score of 0.25 exceeds its Task-1 score of 0.23, which is possible if Task-2 is conditioned on the refusal subset). More importantly, the actual success rate of the two-stage protocol is the joint probability of selecting 'I do not know' and correctly justifying it as a false premise; for Claude this is 0.46 × 0.23 ≈ 0.11, not the 0.23 reported as the Task-2 score. The paper should report this joint success rate as the primary detection metric and clearly state the denominator for each task in the table.
[3.3 and 5] Claude 3.5 Sonnet, which achieves the highest Task-1 score, is the same model family used for question generation. The paper mentions this in Section 5 but does not analyze the potential generator-evaluator advantage. Since the model may be biased toward recognizing the false premises it helped create, the relative ranking of models and the interpretation of Claude's high accuracy are confounded. The authors should either evaluate at least one model not used in generation, or provide a quantitative analysis of how generation affects Claude's performance (e.g., by having a different model generate a sample of questions and comparing scores). This is important for the validity of the cross-model comparison.

minor comments (7)

[3.1] The text states 'The five main types are Property, Event, Entity, and Scope', but only four types are listed; Table 18 also lists four. Correct the count or add the missing type.
[Appendix C] Cross-references to tables are inconsistent: Section 3.1 refers to Table 11 as false-premise types, but Table 11 shows multi-hop types; Table 18 shows false-premise types. Please fix the references.
[Table 19] The first column header 'File' should be 'Category' to match the row content.
[Table 7] The JSON example output is missing a comma between the 'explanation' and 'answer' fields; the example is also not a valid JSON object as written.
[Table 4] The caption contains a typo: 'knowldege' should be 'knowledge'.
[5] The sentence 'Table 3 presents model accuracy on the first group of tasks, which are the first two tasks' is redundant and should be rewritten.
[4] The paper states all experiments used a zero temperature setting; please clarify how this is operationalized for API-based models (e.g., GPT-4o, Gemini) whose temperature may not be directly controllable.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MultiHoax is an empirically constructed benchmark with no fitted parameters or derivational claims that reduce to their inputs.

full rationale

This paper introduces a dataset and reports model performance on it; there is no mathematical derivation, no fitted parameter, and no prediction that is equivalent to an input by construction. The central rule that 'I do not know' is the only correct answer for a false-premise question is a labeling convention of FPQ benchmarks, not a hidden fit: the dataset items were generated, then reviewed against Wikipedia, and the model scores are measured against that externally grounded annotation. The fact that Claude was used to generate questions and also obtains the highest accuracy is a disclosed evaluation bias, not circularity, because the score is not derived from the generation procedure. The only self-citation (Saffari et al., 2025) appears in related work as one example of multicultural norms research and is not load-bearing for the dataset's validity or the experimental conclusions. The appendix reports that 124 of 700 items were initially not confirmed as containing false information and were subsequently modified without a reported third-round re-verification; this is a data-quality and validation limitation, not a circularity, because the modified items' ground truth is not defined in terms of the model outputs. No equations, fitted values, or self-citation chains force the reported results, so the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim that LLMs struggle with MultiHoax depends on several design choices and background assumptions: the hand-picked number of countries, categories, and questions per cell; the reliance on Wikipedia as ground truth; the use of borrowed taxonomies for annotation; and the two-stage evaluation protocol for detecting false premises. These are not fitted parameters, but they are assumptions that should be validated with human baselines and agreement metrics.

free parameters (5)

Number of countries = 7
Hand-chosen to give geographic diversity; the paper notes this could be expanded.
Number of knowledge categories = 10
Hand-chosen; the paper acknowledges possible expansions.
Questions per country-category = 10
Fixed dataset size; yields 700 total questions.
Facts extracted per Wikipedia document = 15
Hand-specified in the fact extraction prompt; affects the pool of candidate facts.
Wikipedia pages per country-category = 15
Hand-specified in the document retrieval prompt; determines coverage.

assumptions (4)

domain assumption Wikipedia is an accurate and sufficient knowledge source for verifying factual claims.
All false-premise verification is performed by checking the associated Wikipedia page; if Wikipedia contains errors or lacks coverage, the dataset's falsehood labels are unreliable.
domain assumption The taxonomy of false premise types from Hu et al. (2023) and the multi-hop types from Mavi et al. (2022) apply cleanly to the generated questions.
The paper uses these taxonomies for annotation and analysis without validating their applicability to multi-hop generated questions.
domain assumption Human annotators can reliably distinguish globally false premises from unverifiable or context-dependent claims.
The curation and second review rely on human judgment; no agreement statistics are reported, so the reliability of this assumption is unknown.
domain assumption The two-stage evaluation protocol (select 'I do not know', then justify by citing a false premise) is a valid measure of false-premise detection.
The protocol is designed to separate uncertainty from false-premise detection, but no validation against human behavior is provided.

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Cite this review

Pith. "Pith review of MultiHoax: A Dataset of Multi-hop False-Premise Questions." pith.science (2026). https://pith.science/paper/Y4DHJPMO

@misc{pith2026250600264,
  author       = {Pith},
  title        = {Pith review of: MultiHoax: A Dataset of Multi-hop False-Premise Questions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y4DHJPMO}},
  note         = {Machine review of arXiv:2506.00264}
}

read the original abstract

As Large Language Models are increasingly deployed in high-stakes domains, their ability to detect false assumptions and reason critically is crucial for ensuring reliable outputs. False-premise questions (FPQs) serve as an important evaluation method by exposing cases where flawed assumptions lead to incorrect responses. While existing benchmarks focus on single-hop FPQs, real-world reasoning often requires multi-hop inference, where models must verify consistency across multiple reasoning steps rather than relying on surface-level cues. To address this gap, we introduce MultiHoax, a benchmark for evaluating LLMs' ability to handle false premises in complex, multi-step reasoning tasks. Our dataset spans seven countries and ten diverse knowledge categories, using Wikipedia as the primary knowledge source to enable factual reasoning across regions. Experiments reveal that state-of-the-art LLMs struggle to detect false premises across different countries, knowledge categories, and multi-hop reasoning types, highlighting the need for improved false premise detection and more robust multi-hop reasoning capabilities in LLMs.

Figures

Figures reproduced from arXiv: 2506.00264 by the authors.

**Figure 1.** A sample MHFPQ from the Sports category related to Iran. False premises can appear in various forms, such as misleading statements, logically inconsistent claims, or factually incorrect contextual narratives (Leite et al., 2023; Zhuang et al., 2023; Ghosh et al., 2024; Galitsky et al., 2024; Chen and Shu, 2023; Yamin et al., 2024; Zhang et al., 2024b; Csönge, 2015). A common approach to evaluating an LLM’s ability … view at source ↗

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Reference graph

Works this paper leans on

19 extracted references · 13 canonical work pages

[1]

The year when Ehsan Rouzbahani won the Olympic bronze medal in Tokyo

work page
[2]

The president of Iran at that time. •Geographical Multi-Hop Reasoning Example: Which football team with the most championships in the territory Alexander the Great conquered before turning 18? Description: Alexander the Great did not conquer any territory before turning 18. Reasoning Steps:

work page
[3]

In both durations, CERN’s affiliates won only 1 Nobel prize

work page
[4]

Naoaki Okazaki, Keita Nabeshima, Kento Watanabe, Junta Mizuno, and Kentaro Inui

The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2. Naoaki Okazaki, Keita Nabeshima, Kento Watanabe, Junta Mizuno, and Kentaro Inui. 2013. Extracting and aggregating false information from microblogs. InProceedings of the Workshop on Language Pro- cessing and Crisis Information 2013, pages 36–43, Nagoya, Japan. Asian Federation of Natural Lan- ...

arXiv 2013
[5]

Andrew F Smith

Culturebank: An online community-driven knowledge base towards culturally aware language technologies.arXiv preprint arXiv:2404.15238. Andrew F Smith. 2001. False memories: The invention of culinary fakelore and food fallacies. InFood and the Memory: Proceedings of the Oxford Symposium on Food and Cookery, pages 254–260. Guijin Son, Hanwool Lee, Sungdong ...

arXiv 2001
[6]

Yixuan Tang and Yi Yang

Kmmlu: Measuring massive multitask lan- guage understanding in korean.arXiv preprint arXiv:2402.11548. Yixuan Tang and Yi Yang. 2024. Multihop-rag: Bench- marking retrieval-augmented generation for multi- hop queries.arXiv preprint arXiv:2401.15391. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean- Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkw...

arXiv 2024
[9]

The territory Alexander the Great conquered before turning 18

work page
[10]

Football team with the most championships in that territory. •Named Entity Multi-Hop Reasoning Example: What is the camera brand used by Spielberg when filming his Academy Award-winning student film at USC? Description: Spielberg never attended USC and didn’t win an Academy Award as a student. Reasoning Steps:

work page

Show all 19 references

[11]

The Spielberg’s Academy Award-winning student film at USC
[12]

Table 14: The second part of the generation prompt for the first group

The camera brand used for that film. Table 14: The second part of the generation prompt for the first group. •Intersection-Type Multi-Hop Reasoning Example: Which architect both designed the golden-domed Old Basilica and incorporated Aztec symbols in its facade in 1695? Descri...
[13]

The architect who designed the golden-domed Old Basilica
[14]

The architect who incorporated Aztec symbols in the Old Basilica’s facade in 1695. •Comparison-Type Multi-Hop Reasoning Example: Which of the Chinese and the Germans first invented sauerkraut in the 18th century? Description: Sauerkraut was not invented in the 18th century, an...
[15]

Did the Chinese invented sauerkraut in the 18th century?
[16]

How many eyes does the sun have?

Did Germans invented sauerkraut in the 18th century? Note: In Comparison type, multi-hop reasoning questions, none of the entities satisfies the condition. Table 15: The second part of the generation prompt for the second group. Your task is to extract false premise multi-hop ...

1985
[18]

1984, 1992, and 2013 are the years when CERN’s affiliates won the award

I don’t know Explanation: In none of the durations, CERN’s affiliates won a Nobel prize. 1984, 1992, and 2013 are the years when CERN’s affiliates won the award. As you can see, such false information types are not detectable unless the person knows about the history of the me...

1984
[19]

There is false information

I don’t know Description: The question contains a false premise that a new Humanistic Buddhist organization was established in Beijing in the 2000s to promote the revival of Vajrayana Buddhism. According to the facts, the Humanistic Buddhist movement in China is associated wit...

1991
[2021]

InProceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing, pages 7052–7063, Online and Punta Cana, Do- minican Republic

Entity-based knowledge conflicts in question answering. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing, pages 7052–7063, Online and Punta Cana, Do- minican Republic. Association for Computational Linguistics. Guanghui Ma, Chunming Hu...

2021 arXiv
[2022]

InProceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, pages 2292–2307, Abu Dhabi, United Arab Emirates

Rich knowledge sources bring complex knowl- edge conflicts: Recalibrating models to reflect con- flicting evidence. InProceedings of the 2022 Con- ference on Empirical Methods in Natural Language Processing, pages 2292–2307, Abu Dhabi, United Arab Emirates. Association for Com...

2022 arXiv
[2024]

Petko Dimov

Syn-QA2: Evaluating false assumptions in long-tail questions with synthetic qa datasets.arXiv preprint arXiv:2403.12145. Petko Dimov. 2021. Recognition of fake news in sports. 29(4s):18–27. Yi Fung, Ruining Zhao, Jae Doo, Chenkai Sun, and Heng Ji. 2024. Massively multi-cultura...

2021 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

The year when Ehsan Rouzbahani won the Olympic bronze medal in Tokyo

work page

[2] [2]

The president of Iran at that time. •Geographical Multi-Hop Reasoning Example: Which football team with the most championships in the territory Alexander the Great conquered before turning 18? Description: Alexander the Great did not conquer any territory before turning 18. Reasoning Steps:

work page

[3] [3]

In both durations, CERN’s affiliates won only 1 Nobel prize

work page

[4] [4]

Naoaki Okazaki, Keita Nabeshima, Kento Watanabe, Junta Mizuno, and Kentaro Inui

The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2. Naoaki Okazaki, Keita Nabeshima, Kento Watanabe, Junta Mizuno, and Kentaro Inui. 2013. Extracting and aggregating false information from microblogs. InProceedings of the Workshop on Language Pro- cessing and Crisis Information 2013, pages 36–43, Nagoya, Japan. Asian Federation of Natural Lan- ...

arXiv 2013

[5] [5]

Andrew F Smith

Culturebank: An online community-driven knowledge base towards culturally aware language technologies.arXiv preprint arXiv:2404.15238. Andrew F Smith. 2001. False memories: The invention of culinary fakelore and food fallacies. InFood and the Memory: Proceedings of the Oxford Symposium on Food and Cookery, pages 254–260. Guijin Son, Hanwool Lee, Sungdong ...

arXiv 2001

[6] [6]

Yixuan Tang and Yi Yang

Kmmlu: Measuring massive multitask lan- guage understanding in korean.arXiv preprint arXiv:2402.11548. Yixuan Tang and Yi Yang. 2024. Multihop-rag: Bench- marking retrieval-augmented generation for multi- hop queries.arXiv preprint arXiv:2401.15391. Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean- Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkw...

arXiv 2024

[7] [9]

The territory Alexander the Great conquered before turning 18

work page

[8] [10]

Football team with the most championships in that territory. •Named Entity Multi-Hop Reasoning Example: What is the camera brand used by Spielberg when filming his Academy Award-winning student film at USC? Description: Spielberg never attended USC and didn’t win an Academy Award as a student. Reasoning Steps:

work page