Summary of Vlind-bench: Measuring Language Priors in Large Vision-language Models, by Kang-il Lee et al.
VLind-Bench: Measuring Language Priors in Large Vision-Language Models
by Kang-il Lee, Minbeom Kim, Seunghyun Yoon, Minsung Kim, Dongryeol Lee, Hyukhun Koh, Kyomin Jung
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Vision-Language Models (LVLMs) have shown remarkable performance across various tasks. However, they suffer from “language prior” issues, where responses are generated based solely on textual patterns, ignoring image information. This problem can lead to biases or hallucinations when dealing with out-of-training distribution images. Current methods for measuring language priors in LVLMs are poorly studied. We propose the VLind-Bench benchmark, which specifically measures language priors (or blindness) of LVLMs. The benchmark includes counterfactual image tests and evaluates basic capabilities like commonsense knowledge, visual perception, and biases. Our analysis reveals that most recent LVLMs exhibit significant reliance on language priors, posing a challenge in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to describe an image using just words – not very accurate, right? Large computer models can do this well, but they often rely too much on what they’ve learned from text, ignoring the actual picture. This is called “language prior” and it’s a problem. We need better ways to measure how well these models really understand images. Our new benchmark, VLind-Bench, helps by testing computer models’ ability to recognize basic things like common sense, visual details, and even biases. Surprisingly, many popular computer models are too reliant on language prior and struggle with real image understanding. |
