Confident but Conflicted: Internal Uncertainty and Cognitive Dissonance Resolution in LLMs

• URL: http://arxiv.org/abs/2606.22633v1
• Date: Sun, 21 Jun 2026 18:39:24 GMT
• Title: Confident but Conflicted: Internal Uncertainty and Cognitive Dissonance Resolution in LLMs
• Abstract summary: Large language models (LLMs) frequently encounter inputs that disagree with their prior outputs.<n>Dissonance can be resolved through persuasion, backfire, or immunity.<n>Trust Elasticity (TE) is an econometrics-inspired measure of how readily a model is persuaded toward conflicting evidence.
• Score: 6.968805961712596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. To study this systematically, we vary persuasion attempts along two dimensions, source authority and evidence quality, across 12 health-science claims of stratified epistemic status. Dissonance can be resolved through persuasion, backfire, or immunity. We introduce Trust Elasticity (TE), an econometrics-inspired measure of how readily a model is persuaded toward conflicting evidence. Across four LLMs, TE varies substantially, while clearly false claims elicit near-zero TE across all models. On two open-weight models, we further find that this variation is associated with two complementary internal uncertainty indicators, Confidence Miscalibration in Qwen and Internal Uncertainty Change in Llama. These results link cross-model behavioral variation to a measurable internal property and point to interventions targeting internal uncertainty as future work.

Related papers

• When Confidence Signals Disagree: Local and Global Confidence in Autoregressive Language Models [0.0]
  We investigate whether different confidence readouts are empirically interchangeable in an autoregressive language model.<n>Across MMLU and ARC Challenge, the two signals are weakly correlated and differ substantially in their association with correctness.<n>Larger local--global confidence gaps are associated with higher answer entropy, more distinct sampled answers, and lower modal-answer concentration.
  arXiv  Detail & Related papers  (2026-09-15T10:08:48Z)
• LLMs Show No Signs Of Individuated Metacognition [0.023227405857540805]
  We decompose binary confidence judgements from 20 Large Language Models.<n>We ask whether two models that differ in confidence also differ in performance.<n>We find no evidence for significant verbalised individuated metacognition in any tested domain.
  arXiv  Detail & Related papers  (2026-05-22T23:54:33Z)
• Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models [54.041320081289996]
  We present the first systematic empirical study of uncertainty estimation in audio-aware large language models (ALLMs)<n>We benchmark five representative methods, including predictive entropy, length-normalized entropy, semantic entropy, discrete semantic entropy, and P(True)<n>Our results reveal two key findings. First, semantic-level and verification-based methods consistently outperform token-level baselines on general audio reasoning benchmarks.
  arXiv  Detail & Related papers  (2026-04-28T12:56:22Z)
• ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation [19.616905640490252]
  We propose a new framework called ERA (Evidence-based Reliability Alignment) to enhance abstention behavior in RAG systems.<n>Our approach significantly outperforms baselines, optimizing the trade-off between answer coverage and abstention with superior calibration.
  arXiv  Detail & Related papers  (2026-02-24T01:45:51Z)
• Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification [27.02252748004729]
  Large vision-language models (LVLMs) have shown substantial advances in multimodal understanding and generation.<n>They frequently produce unreliable or even harmful content, such as fact hallucinations or dangerous instructions.<n>Evidential Uncertainty Quantification (EUQ) captures both information conflict and ignorance for effective detection of LVLM misbehaviors.
  arXiv  Detail & Related papers  (2026-02-05T10:51:39Z)
• Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know" [47.930782177987446]
  Large language models often struggle to recognize their knowledge limits in closed-book question answering, leading to confident hallucinations.<n>We evaluate three task-equivalent prompting regimes: Direct, Assistive, and Incremental, across different model scales and multi-hop QA benchmarks.<n>Because factual knowledge is stable while hallucinations are agreement, cross-regime provides a precise signal of internal uncertainty.
  arXiv  Detail & Related papers  (2026-02-04T18:39:58Z)
• Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs [13.982395477368396]
  Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge.<n>They are still prone to hallucinations, generating incorrect or misleading information, often accompanied by high uncertainty.<n>We introduce Semantic Volume, a novel measure for quantifying both external and internal uncertainty in LLMs.
  arXiv  Detail & Related papers  (2025-02-28T17:09:08Z)
• ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation [91.20492150248106]
  We investigate the internal mechanisms behind unfaithful generation and identify a subset of mid-to-deep feed-forward networks (FFNs) that are disproportionately activated in such cases.<n>We propose Parametric Knowledge Muting through FFN Suppression (ParamMute), a framework that improves contextual faithfulness by suppressing the activation of unfaithfulness-associated FFNs.<n> Experimental results show that ParamMute significantly enhances faithfulness across both CoFaithfulQA and the established ConFiQA benchmark, achieving substantial reductions in reliance on parametric memory.
  arXiv  Detail & Related papers  (2025-02-21T15:50:41Z)
• ECon: On the Detection and Resolution of Evidence Conflicts [56.89209046429291]
  The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems. This study introduces a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios.
  arXiv  Detail & Related papers  (2024-10-05T07:41:17Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)
• Disentangling Observed Causal Effects from Latent Confounders using Method of Moments [67.27068846108047]
  We provide guarantees on identifiability and learnability under mild assumptions. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
  arXiv  Detail & Related papers  (2021-01-17T07:48:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.