Mixed Signals: Decoding VLMs’ Reasoning and Underlying Bias in Vision-Language Conflict (2025.findings-emnlp)
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| Challenge: | Vision-language models have demonstrated impressive performance by effectively integrating visual and textual information to solve complex tasks. |
| Approach: | They build upon existing benchmarks to create five datasets containing mismatched image-text pairs and examine how they reason over visual and textual data . |
| Outcome: | The proposed model reasoned over visual and textual data in real-world applications but not in the visual and visual descriptions. |
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| Challenge: | Current methods to learn biases from the perspective of model components are limited by their complexity and performance. |
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| Challenge: | Vision-language models integrate textual and visual information, enabling them to process visual inputs and generate predictions. |
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Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs (2026.eacl-long)
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| Challenge: | Existing studies have shown that Vision-Language Models have robust multimodal reasoning capabilities, but their robustness against textual misinformation remains under-explored. |
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| Challenge: | Existing studies on VLM bias focus on portrait-style images and gender-occupation associations . existing studies ignore broader and more complex social stereotypes and their implied harm . |
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| Challenge: | Existing studies on social biases focus on a limited set of documented associations, such as gender-profession or race-crime. |
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| Challenge: | Existing studies have highlighted the existence of social biases within large vision and language models. |
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| Challenge: | Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. |
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| Challenge: | Large-scale, pretrained vision-language models are growing in popularity due to impressive performance on downstream tasks with minimal finetuning. |
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What’s Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning (2026.eacl-long)
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| Challenge: | Existing benchmarks often include a mixture of reasoning questions, making it difficult to truly assess VLMs’ causal reasoning abilities. |
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Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)
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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.) |
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