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

• URL: http://arxiv.org/abs/2506.00264v2
• Date: Wed, 04 Jun 2025 13:23:25 GMT
• Title: MultiHoax: A Dataset of Multi-hop False-Premise Questions
• Authors: Mohammadamin Shafiei, Hamidreza Saffari, Nafise Sadat Moosavi,
• Abstract summary: We introduce MultiHoax, a benchmark for evaluating Large Language Models' ability to handle false premises in complex, multi-step reasoning tasks.<n>Our dataset spans seven countries and ten diverse knowledge categories, using Wikipedia as the primary knowledge source.<n> Experiments reveal that state-of-the-art LLMs struggle to detect false premises across different countries, knowledge categories, and multi-hop reasoning types.
• Score: 10.301985230669684
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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