Large language models (LLMs) frequently encounter inputs that disagree with their prior outputs, through user pushback, retrieved documents, or web search results. While the way they resolve such conflicts -- a process we frame as cognitive dissonance resolution -- has been characterized behaviorally, its connection to internal model uncertainty is not well understood.
arXiv:2606. 05403v1 Announce Type: new Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions.
arXiv:2608.29803v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...
arXiv:2608. 14630v1 Announce Type: cross Abstract: Human decision-making is often shaped by a range of well-documented cognitive biases.
The study examines how language models (LMs) alter the expressed certainty of statements when rewriting text, a process termed certainty distortion. Using an LM‑based metric aligned with human judgments, the authors find that up to 75% of LM outputs exhibit such distortion, with most models more likely to inflate certainty than reduce it. Repeated paraphrasing can amplify this effect, especially in medical contexts, and while prompt interventions help, they do not fully eliminate the bias.
arXiv:2607. 01951v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly consulted on contested scientific questions, raising the concern that they will sycophantically retreat from established consensus when a user signals doubt -- drifting toward a false balance that treats settled science as one view among several.
The paper introduces a prompt-response concept model that links the amount of task-relevant information in a prompt to the uncertainty of responses generated by large language models (LLMs). It identifies four sources of response uncertainty—prompt underspecification, model quality, task variability, and semantic redundancy—and demonstrates that uncertainty decreases as prompt informativeness or model quality increases, analogous to epistemic uncertainty in probabilistic models. Experiments on real-world datasets confirm the theoretical predictions and validate the model.
arXiv:2609.07879v1 Announce Type: new Abstract: Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answ...
arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.
arXiv:2607. 19367v1 Announce Type: new Abstract: Calibration is the primary criterion for evaluating LLM confidence, but it is insufficient: it admits trivially incoherent estimators, depends on the evaluation distribution, and does not test the extent to which the estimation can be interpreted as a consistent, underlying probability function.
The study compares communication styles of large language models (LLMs) and humans in explaining COVID‑19 misinformation, using a dataset of 1,498 fact‑checking claims and 99 blinded reader evaluations. LLM‑generated explanations scored lower on persuasive strategies, certainty, and alignment with social values, yet over 60% of participants preferred LLM content for clarity, completeness, and persuasiveness. The findings suggest that reader preference may not align with traditional measures of communication quality, highlighting both the promise and limits of LLMs in health communication.
arXiv:2607. 19355v1 Announce Type: new Abstract: LLMs are increasingly used with external knowledge sources like the internet.
arXiv:2608.17809v2 Announce Type: replace Abstract: Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevita...