Summary of Trust but Verify: Programmatic Vlm Evaluation in the Wild, by Viraj Prabhu et al.
Trust but Verify: Programmatic VLM Evaluation in the Wild
by Viraj Prabhu, Senthil Purushwalkam, An Yan, Caiming Xiong, Ran Xu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a new benchmarking paradigm, Programmatic VLM Evaluation (PROVE), to reliably evaluate Vision-Language Models’ (VLMs) free-form responses to open-ended queries. The PROVE framework involves generating diverse question-answer (QA) pairs and programs that can be executed over a high-fidelity scene-graph representation constructed from a hyper-detailed image caption. This allows for visually verifying each claim within the response, enabling the quantification of hallucinations in VLM responses. A benchmark of 10.5k challenging but visually grounded QA pairs is constructed, showcasing the effectiveness of PROVE in evaluating the helpfulness-truthfulness trade-offs of various VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well computers can answer questions about pictures. The test asks the computer to come up with different question-answer pairs and then checks if the answers are true or not. This helps us figure out when the computer is making things up that aren’t really in the picture. The test has thousands of challenging questions, and it shows that most computers are pretty bad at balancing being helpful with being truthful. |
