Summary of Multimodal Llms Struggle with Basic Visual Network Analysis: a Vna Benchmark, by Evan M. Williams and Kathleen M. Carley
Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark
by Evan M. Williams, Kathleen M. Carley
First submitted to arxiv on: 10 May 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 evaluates the zero-shot ability of two vision language models (VLMs), GPT-4 and LLaVa, to perform simple Visual Network Analysis (VNA) tasks on small-scale graphs. The models are tested on five tasks related to three foundational network science concepts: identifying nodes with maximal degree, determining signed triad balance, and counting graph components. While GPT-4 outperforms LLaVa consistently, both models struggle with each VNA task proposed. To facilitate further research, the authors publicly release a benchmark for evaluating VLMs on these tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well two special kinds of computer programs can understand simple pictures of networks. The programs, GPT-4 and LLaVa, are really good at understanding words and text, but not so much with pictures. The researchers tested the programs on five different things: finding the most important parts of a network, figuring out if certain groups in a network are balanced or unbalanced, and counting how many parts a network has. Both programs struggled to do these tasks correctly. To help other scientists study this topic further, the researchers shared their results publicly. |
Keywords
» Artificial intelligence » Gpt » Zero shot
