[2401.00761] Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2401.00761 (cs) [Submitted on 1 Jan 2024 ( v1 ), last revised 29 Apr 2026 (this version, v2)] Title: Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models Authors: Wenxuan Wang , Yuk-Kit Chan , Zixuan Ling , Juluan Shi , Youliang Yuan , Jen-tse Huang , Yifei Zhang , Wenxiang Jiao , Zhaopeng Tu , Michael R. Lyu View a PDF of the paper titled Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models, by Wenxuan Wang and 9 other authors View PDF HTML (experimental) Abstract: Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education to mislead users. Current methods for evaluating LLMs' veracity are limited by the need for extensive human labor, test data contamination, or limited scope, hindering efficient and effective exposure of errors. To address these challenges, we propose HalluHunter, a novel, fully automated framework for systematically uncovering factual inaccuracies in LLMs. HalluHunter employs a knowledge-graph-based approach, extracting fact triplets to generate diverse question types for single- and multi-hop reasoning using rule-based Natural Language Processing (NLP) techniques. Its iterative process starts with random triplet selection for question generation, followed by adaptive selection in subsequent iterations, targeting triplets where LLMs frequently err based on their performance analysis. Our extensive tests on nine prominent LLMs reveal that HalluHunter can trigger factual errors in up to 55% of tested questions. Moreover, we demonstrate that HalluHunter's test cases, particularly in adaptive selection, could further expose the weaknesses in benchmarking the factuality in LLMs meanwhile maintaining the coverage of questions. All code, data, and results are available at this link: this https URL . Comments: Accepted by Findings of ACL 2026 Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2401.00761 [cs.SE] (or arXiv:2401.00761v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2401.00761 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wenxuan Wang [ view email ] [v1] Mon, 1 Jan 2024 14:02:27 UTC (4,165 KB) [v2] Wed, 29 Apr 2026 05:14:57 UTC (669 KB) Full-text links: Access Paper: View a PDF of the paper titled Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models, by Wenxuan Wang and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2024-01 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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