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    <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 (LLaVA-1.5-7B and 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.</abstract>
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%0 Conference Proceedings
%T Taming Object Hallucinations with Verified Atomic Confidence Estimation
%A Liu, Jiarui
%A Xuan, Weihao
%A Jin, Zhijing
%A Diab, Mona T.
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F liu-etal-2026-taming
%X 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 (LLaVA-1.5-7B and 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.
%U https://aclanthology.org/2026.eacl-long.252/
%P 5430-5444

Markdown (Informal)

[Taming Object Hallucinations with Verified Atomic Confidence Estimation](https://aclanthology.org/2026.eacl-long.252/) (Liu et al., EACL 2026)

• Taming Object Hallucinations with Verified Atomic Confidence Estimation (Liu et al., EACL 2026)

ACL

• Jiarui Liu, Weihao Xuan, Zhijing Jin, and Mona T. Diab. 2026. Taming Object Hallucinations with Verified Atomic Confidence Estimation. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5430–5444, Rabat, Morocco. Association for Computational Linguistics.