Summary of Mllm-compbench: a Comparative Reasoning Benchmark For Multimodal Llms, by Jihyung Kil et al.
MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs
by Jihyung Kil, Zheda Mai, Justin Lee, Zihe Wang, Kerrie Cheng, Lemeng Wang, Ye Liu, Arpita Chowdhury, Wei-Lun Chao
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces MLLM-CompBench, a benchmark designed to evaluate the comparative reasoning capability of multimodal large language models (MLLMs). The benchmark consists of around 40K image pairs, paired through visually oriented questions covering eight dimensions of relative comparison. These questions are carefully crafted to discern relative characteristics between two images and are labeled by human annotators for accuracy and relevance. The paper uses MLLM-CompBench to evaluate recent MLLMs, including GPT-4V(ision), Gemini-Pro, and LLaVA-1.6, revealing notable shortcomings in their comparative abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a tool that helps machines understand how things are different or similar. This is important for making good decisions and solving problems. The tool, called MLLM-CompBench, pairs images together based on what makes them similar or different. It uses questions to help the machine figure out how the two images compare. The paper tested some computer models using this tool and found that they weren’t very good at comparing things. |
Keywords
» Artificial intelligence » Gemini » Gpt
