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<p>The spread of misinformation and disinformation on social media platforms has made automatic claim verification an important concern in various domains. We study the problem of claim verification in the context of claims about fictional stories for the purpose of uncovering logical inconsistencies, also known as plot holes. To this end, we first introduce FicClaim, a synthetic dataset containing plot holes. FicClaim is generated in part by large language models (LLMs) for learning how to apply claim verification to fictional settings. We then develop the FicVer algorithm for finding inconsistencies in a story based on our dataset. We benchmark our algorithm against various claim verification methods and demonstrate that the proposed algorithm leads to state-of-the-art performance. Our code is available at <a href="https://github.com/Derposoft/plot_hole_detection_next">https://github.com/Derposoft/plot_hole_detection_next</a>.</p>

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FicClaim: A Framework for Claim Verification in Fictional Domains Using Synthetic Data Generation

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Viswanath Chadalapaka,
  • Mohammad Rostami

摘要

The spread of misinformation and disinformation on social media platforms has made automatic claim verification an important concern in various domains. We study the problem of claim verification in the context of claims about fictional stories for the purpose of uncovering logical inconsistencies, also known as plot holes. To this end, we first introduce FicClaim, a synthetic dataset containing plot holes. FicClaim is generated in part by large language models (LLMs) for learning how to apply claim verification to fictional settings. We then develop the FicVer algorithm for finding inconsistencies in a story based on our dataset. We benchmark our algorithm against various claim verification methods and demonstrate that the proposed algorithm leads to state-of-the-art performance. Our code is available at https://github.com/Derposoft/plot_hole_detection_next.