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overconfidence problem

The overconfidence problem refers to a calibration failure in machine learning where a model assigns excessively high probability or certainty to outputs that are incorrect or factually inaccurate. In neural networks and large language models, this issue arises when predicted confidence scores, token-level probabilities, or verbalized statements of certainty consistently exceed the empirical likelihood of correctness. As a result, systems can present false, ungrounded, or hallucinated information with misleadingly high conviction. This discrepancy hinders uncertainty-based error detection, safety filtering, and abstention mechanisms, thereby undermining the reliability and trustworthiness of automated predictions in high-stakes tasks.

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Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus

Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu

OrganizationsAmazon Web ServicesInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesShanghai Jiao Tong UniversityWestlake University

Why you should read this

Proposes a reference-free hallucination detection framework that evaluates LLM-generated text without extra sampling or external retrieval by modeling uncertainty through keyword filtering, attention-based error propagation, and entity-specific frequency adjustments.

Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world applications. Existing works for detecting hallucinations in LLMs either rely on external knowledge for reference retrieval or require sampling multiple responses from the LLM for consistency verification, making these methods costly and inefficient. In this paper, we propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs. Our approach imitates human focus in factuality checking from three aspects: 1) focus on the most informative and important keywords in the given text; 2) focus on the unreliable tokens in historical context which may lead to a cascade of hallucinations; and 3) focus on the token properties such as token type and token frequency. Experimental results on relevant datasets demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance across all the evaluation metrics and eliminates the need for additional information.

Added

2026-09-26