TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering
Year: 2026
Type: conference-paper
Abstract: Large Language Models (LLMs) are increasingly used to answer factual, informationseeking questions (ISQs).While prior work often focuses on false misleading information, little attention has been paid to true but strategically persuasive content that can derail a model's reasoning.To address this gap, we introduce a new evaluation dataset, TRUTH-TRAP, in two languages, i.e., English and Farsi, on Iran-related ISQs, each paired with a correct explanation and a true-yet-misleading hint.We then evaluate nine diverse LLMs (spanning proprietary and open-source systems) via factuality classification and multiple-choice QA tasks, finding that accuracy drops by 25%, on average, when models encounter these misleading yet factual hints.Also, the models' predictions match the hint-aligned options up to 77 percent of the time.Notably, models often misjudge such hints in isolation yet still integrate them into final answers.Our results highlight a significant limitation in LLM outputs, underscoring the importance of robust fact-verification and emphasizing real-world risks posed by partial truths in domains like social media, education, and policy-making.Our dataset is openly available at https://github.com/Mamin78/ truthtrap_with_code.
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7140137854