Papers with HallusionBench
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. |
Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings (2026.findings-acl)
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Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti, Gauri Jagatap, Jiaxin Yuan, Sarvesh Baskar, Vijay Kamarshi, Andrea Fanelli, Furong Huang
| Challenge: | Hallucinations in Large Vision-Language Models (LVLMs) are a persistent challenge, stemming from inadequate integration of visual information during multimodal reasoning. |
| Approach: | They propose a visual feature incorporation method that encourages the model to learn visually-informed textual embeddings distinct from those of the base LLM and promotes a more balanced attention distribution. |
| Outcome: | The proposed method significantly reduces hallucinations and fosters more balanced multimodal reasoning. |