What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases (2024.naacl-long)
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| Challenge: | Vision-language models have broad competence that is difficult to evaluate . current evaluation benchmarks focus on only assessing one or a few capabilities . |
| Approach: | They perform a large-scale transfer learning experiment to discover latent VL skills from data. |
| Outcome: | The results suggest that factor analysis can identify reasonable yet surprising VL skill factors . the results contribute to the design of balanced and broad-coverage vision-language evaluation methods. |
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| Challenge: | X-VLM models lack "fine-grained" understanding of relationships, verbs and numbers in images . pretraining on large-scale image–text data from the Web has facilitated rapid progress on many vision-and-language tasks . |
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Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals (2025.naacl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) have been proposed to augment LLMs with visual inputs. |
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A Multi-dimensional study on Bias in Vision-Language models (2023.findings-acl)
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| Challenge: | Recent studies have focused on the issue of bias in joint Vision-Language models . pre-trained models complete a neutral template with a hurtful word 5% of the time . |
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Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities (2025.acl-long)
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| Challenge: | Vision-language Models have been shown to be highly capable but lacking basic visual understanding skills. |
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| Challenge: | Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. |
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| Challenge: | Recent studies have focused on the compositionality of vision-language models (VLMs) however, the performance of GVLMs in multimodal compositional reasoning remains under-explored. |
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Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model (2025.acl-long)
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Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, Goran Glavaš
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How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey (2023.findings-emnlp)
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| Challenge: | Transferability estimation has been a topic of great interest in computer vision fields . a lack of a comprehensive comparison between these estimation methods is a problem . |
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An Explainable Toolbox for Evaluating Pre-trained Vision-Language Models (2022.emnlp-demos)
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| Challenge: | Existing studies evaluate VLP models by comparing the fine-tuned downstream task performance with the average downstream task accuracy. |
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VLind-Bench: Measuring Language Priors in Large Vision-Language Models (2025.findings-naacl)
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| Challenge: | Large Vision-Language Models suffer from a problem known as language prior . such language priors can lead to undesirable biases and hallucinations when dealing with images that are out of distribution. |
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