Credal Transformer: A Principled Approach for Quantifying and Mitigating Hallucinations in Large Language Models

• URL: http://arxiv.org/abs/2510.12137v1
• Date: Tue, 14 Oct 2025 04:31:49 GMT
• Title: Credal Transformer: A Principled Approach for Quantifying and Mitigating Hallucinations in Large Language Models
• Authors: Shihao Ji, Zihui Song, Jiajie Huang,
• Abstract summary: Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions.<n>We introduce the Credal Transformer, which replaces standard attention with a Credal Attention Mechanism (CAM) based on evidential theory.
• Score: 9.660348625678001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems from the Transformer's Softmax function, which creates "Artificial Certainty" by collapsing ambiguous attention scores into a single probability distribution, discarding uncertainty information at each layer. To fix this, we introduce the Credal Transformer, which replaces standard attention with a Credal Attention Mechanism (CAM) based on evidential theory. CAM produces a "credal set" (a set of distributions) instead of a single attention vector, with the set's size directly measuring model uncertainty. We implement this by re-conceptualizing attention scores as evidence masses for a Dirichlet distribution: sufficient evidence recovers standard attention, while insufficient evidence yields a diffuse distribution, representing ambiguity. Empirically, the Credal Transformer identifies out-of-distribution inputs, quantifies ambiguity, and significantly reduces confident errors on unanswerable questions by abstaining. Our contribution is a new architecture to mitigate hallucinations and a design paradigm that integrates uncertainty quantification directly into the model, providing a foundation for more reliable AI.

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