M ulti H oax: A Dataset of Multi-hop False-premise questions (2025)

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 cross-regional factual reasoning. 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.

## introduction in recent years, the evolution of large language models (llms) has demonstrated their immense potential across a wide range of natural language processing tasks. however, despite their impressive successes in many domains, their ability to recognize and properly handle false premises in reasoning tasks remains a significant challenge @xcite . when engaging with false premises, an llm should be able to identify and reject flawed assumptions rather than proceed as if they were valid. which iranian wrestler won gold in the men's freestyle 125 kg at the first olympics when zahra nemati was the flag bearer? at which olympics did zahra nemati carry the flag for the first time? the answer: zahra nemati carried the flag for the first time at the 2016 rio olympics. which iranian wrestler won gold in the men's freestyle 125 kg at the 2016 rio olympics? falsehood: no iranian wrestler won gold in the men's freestyle 125 kg at the 2016 rio olympics. the only iranian wrestler to win gold in the men's freestyle at the 2016 rio olympics was hassan yazdani in 74 kg. false premises can appear in various forms, such as misleading statements, logically inconsistent claims, or factually incorrect contextual narratives @xcite @xcite @xcite @xcite . a common approach to evaluating an llm's ability to handle false premises is through question-answering (qa) tasks, where a question implicitly contains a misleading or incorrect assumption @xcite @xcite . for example, a well-performing llm should recognize that "which year did albert einstein win the nobel prize in chemistry?" contains a false premise-einstein never won a nobel prize in chemistry-and responds appropriately, rather than attempting to provide an incorrect or misleading answer. existing benchmarks focus on single-hop false premise detection, where the incorrect assump-tion can be identified within a single reasoning step @xcite @xcite . however, multi-hop reasoning presents a greater challenge, as it requires models to connect multiple pieces of information across multiple inference steps before arriving at an answer @xcite . unlike single-hop questions, multihop questions (mhqs) require models to derive intermediate conclusions, making them a critical area of research in the evaluation and improvement of llm reasoning abilities @xcite @xcite @xcite . the complexity of mhqs arises from the necessity of bridging multiple facts, understanding implicit dependencies between reasoning steps, and maintaining logical consistency throughout the inference process @xcite . while mhqs are already challenging, the problem becomes even more difficult when false premises are embedded within the reasoning chain, requiring models not only to answer questions correctly but also to detect and reject incorrect assumptions at intermediate steps. to evaluate llms' ability to handle this challenge, we introduce a novel benchmark of multi-hop false-premise questions (mhfpqs), called multihoax, that combines the difficulty of false premise questions and multi-hop question answering. mhfpqs are designed to test whether llms can detect false premises that appear at one or more reasoning steps rather than simply providing an answer based on a flawed assumption. figure 1 shows an example of an mhfpq from the sports category related to iran. answering this question requires a structured reasoning process. first, the model must determine the edition of the olympics where zahra nemati served as iran's flag bearer for the first time, namely, the 2016 rio olympics. second, it must identify the iranian wrestler who won the gold medal in the men's freestyle 125 kg category at those olympics. this introduces a false premise: no iranian wrestler won gold in the 125 kg category at the 2016 olympics. the only iranian gold medalist in men's freestyle wrestling at those games was hassan yazdani, who competed in the 74 kg weight class. to ensure realism and difficulty, we carefully designed the set of possible answer choices in the mhfpq dataset. each question includes believable distractors, as possible, that align with historical facts, making the task particularly challenging for llms. for instance, in the example from figure 1 , the distractors are real iranian wrestlers. moreover, ghassem rezaei and komeil ghassemi both won gold in freestyle wrestling at the 2012 london olympics. hassan yazdani also won the gold at the 2016 olympics, but was not competing in the 125 kg division. this ensures that incorrect answers remain plausible while still being contextually invalid. without detecting the false premise, llms may be misled into selecting one of the plausible but incorrect answers, emphasizing the necessity for models to engage in deep contextual reasoning rather than relying on surface-level fact retrieval. the dataset spans 7 countries and 10 distinct categories, ensuring broad diversity across historical, cultural, and geopolitical contexts. to generate the mhfpqs, we extracted relevant information from wikipedia pages with the assistance of the claude model foot_0 . each question is paired with three closely related distractors and a single "i do not know" option, which serves as the only valid response in the presence of a false premise. by structuring the benchmark in this way, we aim to assess llms' ability to identify and reject incorrect assumptions embedded within multi-hop reasoning chains, rather than merely selecting the most superficially plausible response. we conduct a comprehensive evaluation of six open-source and closed-source models using our multihoax dataset of 700 carefully reviewed questions. our results reveal suboptimal performance across all models, categories, and countries, indicating that current systems struggle with detecting falsehoods in multi-hop reasoning tasks. this resource enables rigorous analysis of multi-hop reasoning under false premises and opens new research directions in question answering that require rejection of complex, embedded assumptions. ## related work false information and false premises. false information is a broad and multifaceted issue, encompassing explicitly misleading claims from social networks @xcite , blogs @xcite , news sources @xcite @xcite , and online forums @xcite . however, false premises are a distinct challenge since they are not always deliberate misinformation but rather incorrect assumptions that lead to flawed logical conclusions. these premises span various domains, including historical events @xcite , religion @xcite , and sports @xcite , making them particularly complex to detect. false-premise questions. fpqs assess a model's ability to reason over implicit false assumptions about properties, actions, scope, existence, events, logic, or causality @xcite . for example, "how many eyes does the sun have?" wrongly assumes the sun has eyes @xcite . since fpqs require models to identify and reject flawed premises rather than simply answering the question, llms often fail to recognize implicit falsehoods, leading to misleading or nonsensical responses @xcite . this inability to reject false premises poses risks for misinformation propagation and model alignment with human expectations. early studies on fpqs have focused on obvious falsehoods that humans can easily detect. for instance, @xcite introduced a dataset of simple fpqs, such as "what is the most common color of human's wings?". while earlier llms struggled with these, recent versions of the llms handle them easily, raising the need to evaluate models on more subtle false premises. similarly, @xcite proposed a dataset evaluating models on questions containing false or unverifiable assumptions. however, their dataset includes unverifiable claims-statements that may become true over time. for example, "when is steven universe season 5 coming to hulu?" assumes the event has not yet occurred in order to be false, but this assumption could later become valid. this distinction makes their dataset less suitable for assessing persistent false premises. another line of research has focused on false-presupposition questions extracted from online forums. @xcite introduced a dataset of fpqs sourced from reddit, such as "how exactly is current stored in power plants?"-a misleading assumption since electric current is not stored. however, their dataset is primarily limited to scientific and technical topics, lacking the broad factual diversity required for a general evaluation of false premises. beyond fpqs, unanswerable questions have also been explored in related research. @xcite focus on document-grounded unanswerable questions, where a question lacks supporting information within a given document. their contribution is in evaluating models' ability to reformu-late unanswerable questions into answerable ones, rather than directly assessing how llms handle false premises in an open-domain setting. additionally, @xcite study truthful question answering, focusing on questions that elicit misconceptions from humans. relatedly, @xcite and @xcite examine how llms handle questions beyond their knowledge, where the correct response should be "i do not know". these studies, however, focus on uncertain or conflicting information rather than inherently false premises. multi-hop false-premise reasoning. most fpq benchmarks focus on single-hop false premises, yet real-world misinformation often involves multi-step reasoning, making it significantly harder to detect. multi-hop reasoning questions challenge llms by requiring them to connect multiple facts across inference steps. while extensive research has explored general mhq understanding in llms @xcite @xcite , mhqs embedding false premises remain largely unstudied. existing benchmarks assess multi-hop factual reasoning but do not evaluate whether llms can detect and reject false premises within reasoning chains. this gap is critical, as misinformation is rarely an isolated error-false premises are often interwoven across reasoning steps, making them subtle, plausible, and difficult to refute. evaluating how llms handle multi-hop false premises is essential for enhancing their robustness against misinformation and ensuring logical consistency in complex reasoning tasks. @xcite attempt to address this with adversarial multi-hop false premise questions, modifying hotpotqa @xcite . their approach replaces the title of a supporting document with a similar but unrelated distractor, selected based on shared wikipedia categories. for example, roger o. egeberg and steven k. galson both fall under american public health doctors, making them interchangeable under this method. however, this technique relies on structured entity swaps rather than embedding implicit falsehoods, making it unclear whether a question contains a truly false premise or is merely unverifiable. additionally, this approach can lead to unnatural question phrasing, limiting its applicability in assessing real-world false premise detection. overall, llms struggle with imperfect informa-tion, conflicting evidence, and questions beyond their knowledge scope @xcite @xcite @xcite @xcite @xcite . they are also susceptible to distraction by irrelevant details, including false premises, often leading to incorrect, misleading, or fabricated responses @xcite @xcite @xcite . these findings underscore a critical gap in evaluating multi-step false premise reasoning, as prior benchmarks focus on single-hop fpqs and adversarial perturbations, failing to capture the complexity of false premises embedded within structured factual reasoning. to address this, we introduce multihoax, a new benchmark of multi-hop fpqs, designed to assess llms' ability to detect and reject false premises within reasoning chains. by including implicit false factual claims across diverse country-related topics, our benchmark enables a more nuanced evaluation of llms' ability to detect and reject false premises embedded in complex, multi-hop reasoning chains. ## multihoax dataset this section describes the proposed dataset, designed to evaluate multi-hop false premise reasoning across diverse countries, categories, and question types. the dataset structure enables a systematic analysis of how llms handle false premises within complex reasoning tasks. ## evaluation setup the multihoax dataset serves as the basis for our evaluation of multi-hop false premise reasoning through two primary tasks: (1) closed multiplechoice qa and (2) justification-based verification. in the first task, models were presented with a question and four answer choices, including "i do not know", allowing them to reject the question if they identified a false premise. the prompt used for this step is shown in the first part of in the second task (justification), models that selected "i do not know" are prompted to explain their decision, as shown in the second part of we additionally explore an alternative output format using structured json, where models are asked to produce both an answer and a textual explanation. while this format allows for more expressive reasoning, it can make it harder to unambiguously determine whether a model has identified a false premise, as rejection may be implied rather than explicit. by contrast, the multiple-choice setup includes an explicit "i do not know" option, enabling clearer detection of false-premise rejection. despite the possibility that this option may simplify the task, it provides a more standardized and reliable evaluation framework. models we tested 6 different proprietary and open-source llms in our experiments. the models include claude sonnet 3.5 foot_5 , gemini-2.0-pro-exp @xcite , gpt-4o @xcite , qwen2.5-7b-instruct @xcite , llama-3.1-8b-instruct @xcite , and deepseekllm-7b-chat @xcite . all experiments used a zero temperature setting to ensure deterministic responses, with all data collected in february 2025. the second task analyzes whether models can justify their "i do not know" responses by correctly identifying the false premise. here, gemini slightly outperforms the generator model ( claude but overall accuracy remains low across all models, indicating persistent difficulty in recognizing false premises. here is an example output: { "explanation": "the university of tehran press published the works of hafez when it was founded." "answer": "hafez" } only return the json. ## conclusion we introduce a novel class of multi-hop falsepremise questions (mhfpqs), combining the complexities of multi-hop reasoning and false-premise detection. to support this research, we present multihoax, the first fpq resource that spans multiple countries and knowledge domains, enabling evaluation of llms' ability to navigate multi-step falsehoods in diverse contexts. our dataset provides a comprehensive evaluation framework, spanning seven countries and ten knowledge categories, allowing for a detailed analysis of how llms handle false premises across diverse topics and regions. unlike prior fpq datasets, mhfpqs require deeper reasoning, as falsehoods are not immediately apparent but emerge through multi-step inference. multihoax establishes a novel evaluation setting at the intersection of multi-hop reasoning and falsepremise detection, revealing persistent limitations in current llms. by targeting assumption-level failures across diverse domains and regions, it provides a rigorous benchmark for advancing models that can reason robustly under implicit factual errors.