Summary of A Surprising Failure? Multimodal Llms and the Nlvr Challenge, by Anne Wu et al.
A Surprising Failure? Multimodal LLMs and the NLVR Challenge
by Anne Wu, Kianté Brantley, Yoav Artzi
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 study assesses three leading MLLMs – GPT-4V, Gemini Pro, and open-source model IDEFICS – on the compositional natural language vision reasoning task NLVR. The task involves determining the truth value of a human-written sentence paired with a synthetic image. Despite their strong performance in other areas, these models struggle with NLVR, which demands compositional and spatial reasoning, as well as robustness to semantic and systematic biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how good three popular AI models are at understanding sentences about pictures. It’s like trying to figure out if a sentence is true or false based on what you see in the picture. These models are really good at some things, but they’re not so great at this specific task, which requires thinking carefully and avoiding biases. |
Keywords
* Artificial intelligence * Gemini * Gpt
