Papers with AMBER
Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models (2025.findings-acl)
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| Challenge: | Large Vision Language Models suffer from hallucinations, attributing incorrect or misleading features to images. |
| Approach: | They propose a test-time approach that recalibrates the influence of blind tokens . they identify blind token by analyzing layer-wise attention distributions over image tokens. |
| Outcome: | The proposed approach reduces hallucinations in large vision language models . it uses a contrastive decoding strategy to balance the influence of blind tokens . |
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. |
Explicit Alignment Objectives for Multilingual Bidirectional Encoders (2021.naacl-main)
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| Challenge: | Pre-trained cross-lingual encoders have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resourced languages. |
| Approach: | They propose a method to align multilingual encoders using two explicit alignment objectives that align the multilingual representations at different granularities. |
| Outcome: | The proposed method achieves gains of up to 1.1 average F1 score on sequence tagging and 27.3 average accuracy on retrieval over the XLM-R-large model. |
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. |
LPOI: Listwise Preference Optimization for Vision Language Models (2025.acl-long)
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| Challenge: | Existing methods for aligning large VLMs with human preferences often overfit to textual information or exacerbate hallucinations. |
| Approach: | They propose an object-aware listwise preference optimization for reducing hallucinations in VLMs . they mask a critical object in an image and interpolate the masked region to form more complete images . |
| Outcome: | The proposed method outperforms existing methods in reducing hallucinations and enhancing performance on MMHalBench, AMBER, and Object HalBench. |
Trident: Self-Supervised Preference Alignment via Triplet Regularization (2026.findings-acl)
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| Challenge: | Large vision-Language Models suffer from noisy supervision and semantic ambiguity in self-supervised settings. |
| Approach: | They propose a self-supervised framework that constructs reliable preference triplets . they propose 'trident' objective that enforces semantic separation between the triplet components . |
| Outcome: | The proposed framework outperforms state-of-the-art RLHF and RLAIF benchmarks on LLaVA-1.5-7B and achieves 95.70% precision on POPE using only 4k self-generated triplets and a single epoch. |
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. |