{"ID":2837593,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19166","arxiv_id":"2511.19166","title":"Representational and Behavioral Stability of Truth in Large Language Models","abstract":"Large language models (LLMs) are increasingly used as information sources, yet small changes in semantic framing can destabilize their truth judgments. We propose P-StaT (Perturbation Stability of Truth), an evaluation framework for testing belief stability under controlled semantic perturbations in representational and behavioral settings via probing and zero-shot prompting. Across sixteen open-source LLMs and three domains, we compare perturbations involving epistemically familiar Neither statements drawn from well-known fictional contexts (Fictional) to those involving unfamiliar Neither statements not seen in training data (Synthetic). We find a consistent stability hierarchy: Synthetic content aligns closely with factual representations and induces the largest retractions of previously held beliefs, producing up to $32.7\\%$ retractions in representational evaluations and up to $36.3\\%$ in behavioral evaluations. By contrast, Fictional content is more representationally distinct and comparatively stable. Together, these results suggest that epistemic familiarity is a robust signal across instantiations of belief stability under semantic reframing, complementing accuracy-based factuality evaluation with a notion of epistemic robustness.","short_abstract":"Large language models (LLMs) are increasingly used as information sources, yet small changes in semantic framing can destabilize their truth judgments. We propose P-StaT (Perturbation Stability of Truth), an evaluation framework for testing belief stability under controlled semantic perturbations in representational an...","url_abs":"https://arxiv.org/abs/2511.19166","url_pdf":"https://arxiv.org/pdf/2511.19166v3","authors":"[\"Samantha Dies\",\"Courtney Maynard\",\"Germans Savcisens\",\"Tina Eliassi-Rad\"]","published":"2025-11-24T14:28:50Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}