Summary of Isobench: Benchmarking Multimodal Foundation Models on Isomorphic Representations, by Deqing Fu et al.
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
by Deqing Fu, Ruohao Guo, Ghazal Khalighinejad, Ollie Liu, Bhuwan Dhingra, Dani Yogatama, Robin Jia, Willie Neiswanger
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The paper proposes IsoBench, a benchmark dataset containing problems from four major areas: math, science, algorithms, and games. Each example is presented with multiple isomorphic representations of inputs, such as visual, textual, and mathematical presentations. This allows for fine-grained feedback to diagnose performance gaps caused by the form of the representation. The authors observe that foundation models have a consistent preference towards textual representations. Specifically, Claude-3 Opus performs 28.7 points worse when provided with images instead of text; similarly, GPT-4 Turbo is 18.7 points worse and Gemini Pro is 14.9 points worse. To improve model performance, the authors present two prompting techniques: IsoCombination and IsoScratchPad. These techniques consider combinations of and translations between different input representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well foundation models do when given different types of information to work with. Foundation models are like super smart computers that can understand and respond to text, images, or other forms of data. The authors created a special dataset called IsoBench that has problems from four areas: math, science, algorithms, and games. Each problem is presented in multiple ways, such as text, pictures, or equations. This helps us see how well the models do depending on the type of information they’re given. The results show that most models prefer working with text over images or other forms of data. The authors also suggest some new ways to help models work better by combining different types of input. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Prompting
