From Sampling to Cognition: Modeling Internal Cognitive Confidence in Language Models for Robust Uncertainty Calibration

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet they generally lack self-awareness, often displaying overconfidence when confronted with questions beyond their knowledge boundaries. This limitation severely hinders their trustworthiness in high-stakes scenarios. Existing calibration methods typically rely on sampling accuracy, derived from multiple outputs, as a proxy for model confidence. However, this coarse-grained metric fails to capture the model’s internal cognitive states, such as confusion, hallucination, or persistent belief in false knowledge. To address this, we propose CogConf (Cognitive Confidence), a cognitively grounded uncertainty signal that extends sampling accuracy by incorporating the semantic diversity of incorrect answers and the model’s abstention behaviors. By shifting the focus from sampling-based to cognition-oriented uncertainty modeling, CogConf offers a more faithful reflection of the model's internal beliefs. Building on this signal, we introduce CogAlign, a simple yet effective alignment framework that explicitly aligns the model’s verbalized confidence with CogConf, thereby producing uncertainty estimates that better reflect the model’s internal cognition. Experimental results on six knowledge-intensive in-domain and out-of-domain QA datasets demonstrate that CogConf robustly characterizes the model's internal uncertainty. Building on this foundation, CogAlign guides the model's expression to significantly enhance the trustworthiness and utility of its uncertainty calibration without compromising its underlying QA capabilities, while also demonstrating strong cross-task generalization and output stability. Offering a new pathway toward building more trustworthy LLMs.