Taming Object Hallucinations with Verified Atomic Confidence Estimation (2026.eacl-long)
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| Challenge: | Multimodal Large Language Models suffer from hallucinations, especially errors in object existence, attributes, or relations. |
| Approach: | They propose a framework that decomposes responses into atomic queries and estimates confidence using self-consistency or self-confidence aggregation. |
| Outcome: | Experiments on five benchmarks show that TACO outperforms direct prompting and Visual Contrastive Decoding and improves confidence calibration. |
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