Papers with Large Visual Language Models
Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification (2024.emnlp-main)
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| Challenge: | Large Visual Language Models (LVLMs) suffer from hallucinations due to limited training data, lack of * Equal contribution precise grounding, and over-reliance on language priors. |
| Approach: | They propose a framework to detect and mitigate hallucinations through claim verification using program-of-thought prompting and Python code to generate a graph. |
| Outcome: | The proposed framework improves over baseline LVLMs and existing methods across several benchmarks. |
MVP-Bench: Can Large Vision-Language Models Conduct Multi-level Visual Perception Like Humans? (2024.findings-emnlp)
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| Challenge: | Existing LVLMs perform visual perception at multiple levels, but they are not able to perform multi-level tasks. |
| Approach: | They propose a visual–language benchmark to evaluate LVLMs' perceptions . they use manipulated images to examine how LVLs can perform multi-level tasks . |
| Outcome: | The proposed model performs poorly on high-level perception tasks, the authors show . they also show that current models do not generalize in understanding semantics of synthetic images . |
ECHA: Jailbreaking LVLMs via the Mismatch between Implicit Semantic Reconstruction and Explicit Safety Alignment (2026.findings-acl)
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| Challenge: | Existing safety guardrails fail to intercept latent intent, whereas LVLMs can implicitly synthesize holistic malicious semantics from fragmented visual cues. |
| Approach: | They propose an Emoji Chain Hinting Attack (ECHA) framework that decouples sensitive concepts into semantically related emoji chains and structural text masks. |
| Outcome: | The proposed framework outperforms existing baselines and bypasses safety guardrails in over 81% of instances with a single attempt. |
Visual In-Context Learning for Large Vision-Language Models (2024.findings-acl)
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| Challenge: | Existing approaches to improve the performance of Large Visual Language Models (LVLMs) are limited by cross-modal interactions and representation disparities. |
| Approach: | They propose a Visual In-Context Learning method that retrieves images via a 'Retrieval & Rerank' paradigm and summarises images with task intent and task-specific visual parsing to compose language-based demonstrations that reduce token count. |
| Outcome: | The proposed method reduces token count and alleviates cross-modal interaction problem on visual reasoning datasets. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
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Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang
| Challenge: | Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment. |
| Approach: | They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data . |
| Outcome: | The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech. |
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
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| Challenge: | Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space. |
| Approach: | They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness. |