Papers with CHAIR
ALOHa: A New Measure for Hallucination in Captioning Models (2024.naacl-short)
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Suzanne Petryk, David Chan, Anish Kachinthaya, Haodi Zou, John Canny, Joseph Gonzalez, Trevor Darrell
| Challenge: | Existing metric for object hallucination, CHAIR, is limited to MS COCO objects and synonyms. |
| Approach: | They propose a new open-vocabulary metric, ALOHa, which leverages large language models to measure object hallucinations. |
| Outcome: | The proposed metric correctly identifies 13.6% more hallucinated objects than CHAIR on HAT and 30.8% more on nocaps. |
DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination (2024.emnlp-main)
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| Challenge: | Despite the success of Large Vision-Language Models, they suffer from hallucination. |
| Approach: | They propose a training-free strategy that "D**ive into" the attention of LVLMs to "R**educe" object hallucination by using classification tokens of ViT. |
| Outcome: | The proposed method reduces the impact of outlier tokens on LVLMs . the proposed method is based on LLaVA-1.5, LLvaVA-NeXT and InstructBLIP . |
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation (2026.findings-acl)
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Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Cao Liu, Ke Zeng, Fengying Xie, Zhiguo Jiang, Haopeng Zhang
| Challenge: | Large vision–language models suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence. |
| Approach: | They propose a framework that allocates visual computation by uncertainty . they propose highlighting retains global context, while selective zoom-in performs local verification. |
| Outcome: | The proposed framework reduces the complexity of multimodal reasoning by minimizing the operator trade-off. |
MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding (2025.findings-emnlp)
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| Challenge: | LVLMs have shown remarkable performance in visual-language understanding for downstream multimodal tasks. |
| Approach: | They propose a method to alleviate hallucinations by masking the “image heads” in LVLMs . |
| Outcome: | The proposed method alleviates the phenomenon of hallucinations and retains the general capabilities of LVLMs. |
Inject to Heal: Alleviating hallucination in LVLMs via Context Embedding Injection (2026.findings-acl)
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| Challenge: | a large vision-language model can generate hallucinations inconsistent with visual input . a lightweight method that embeds the last input token as a grounding signal reduces the likelihood of hallucinosity. |
| Approach: | They propose a training-free mitigation strategy that harnesses the hidden state of the last input token as a grounding signal to maintain visual fidelity throughout decoding and curb hallucinations. |
| Outcome: | The proposed method outperforms state-of-the-art methods on CHAIR, AMBER, and MMHal benchmarks. |