Taming Object Hallucinations with Verified Atomic Confidence Estimation

• URL: http://arxiv.org/abs/2511.09228v1
• Date: Thu, 13 Nov 2025 01:41:44 GMT
• Title: Taming Object Hallucinations with Verified Atomic Confidence Estimation
• Authors: Jiarui Liu, Weihao Xuan, Zhijing Jin, Mona Diab,
• Abstract summary: We introduce TACO, a framework that mitigates hallucinations through self-verification and confidence calibration without relying on external vision experts.<n>TACO decomposes responses into atomic queries, paraphrases them to reduce sensitivity to wording, and estimates confidence using self-consistency (black-box) or self-confidence (gray-box) aggregation.
• Score: 18.54793854791167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) often suffer from hallucinations, particularly errors in object existence, attributes, or relations, which undermine their reliability. We introduce TACO (Verified Atomic Confidence Estimation), a simple framework that mitigates hallucinations through self-verification and confidence calibration without relying on external vision experts. TACO decomposes responses into atomic queries, paraphrases them to reduce sensitivity to wording, and estimates confidence using self-consistency (black-box) or self-confidence (gray-box) aggregation, before refining answers with a language model. Experiments on five benchmarks (POPE, MME, HallusionBench, AMBER, and MM-Hal Bench) with two MLLMs (\texttt{LLaVA-1.5-7B} and \texttt{CogVLM2}) show that TACO consistently outperforms direct prompting and Visual Contrastive Decoding, reduces systematic biases, and improves confidence calibration, demonstrating its effectiveness in enhancing the faithfulness of MLLMs.

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