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Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem

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arxiv 2403.03558 v1 pith:SEKQ6UAD submitted 2024-03-06 cs.CL

Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem

Yuhong Sun , Zhangyue Yin , Qipeng Guo , Jiawen Wu , Xipeng Qiu , Hui Zhao This is my paper
classification cs.CL
keywords hallucinationunanswerablelanguagemathproblemwordapproachcalled
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Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model's ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://github.com/Yuki-Asuuna/UMWP.

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Cited by 3 Pith papers

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  2. Enhancing LLM Metacognition via Cognitive Pairwise Training

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    CPT is introduced as a pairwise reasoning-trace comparison stage that improves the reasoning-metacognition trade-off over standard SFT+RL pipelines across model scales.

  3. Neural Message-Passing on Attention Graphs for Hallucination Detection

    cs.LG 2025-09 conditional novelty 6.0

    CHARM trains graph neural networks on token-attention graphs built from LLM computational traces and outperforms prior hallucination detectors on five benchmarks at token and response level.