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probability correction

Probability correction is the process of adjusting a language model predicted token probabilities or confidence scores to better reflect the true factual reliability and certainty of generated text. In uncertainty estimation and hallucination detection, raw probabilities produced by neural models can become miscalibrated, causing issues such as underconfidence on rare words or unwarranted overconfidence from prior context. Probability correction remedies these discrepancies by recalibrating token-level probabilities based on contextual or linguistic properties, including token frequency, inverse document frequency, and entity types, thereby yielding more dependable uncertainty estimates for assessing the accuracy of generated content.

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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