Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark

• URL: http://arxiv.org/abs/2405.06634v2
• Date: Mon, 10 Jun 2024 15:28:16 GMT
• Title: Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark
• Authors: Evan M. Williams, Kathleen M. Carley,
• Abstract summary: We evaluate the zero-shot ability of GPT-4 and LLaVa to perform simple Visual Network Analysis tasks on small-scale graphs. We find that while GPT-4 consistently outperforms LLaVa, both models struggle with every visual network analysis task we propose.
• Score: 4.112909937203117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We evaluate the zero-shot ability of GPT-4 and LLaVa to perform simple Visual Network Analysis (VNA) tasks on small-scale graphs. We evaluate the Vision Language Models (VLMs) on 5 tasks related to three foundational network science concepts: identifying nodes of maximal degree on a rendered graph, identifying whether signed triads are balanced or unbalanced, and counting components. The tasks are structured to be easy for a human who understands the underlying graph theoretic concepts, and can all be solved by counting the appropriate elements in graphs. We find that while GPT-4 consistently outperforms LLaVa, both models struggle with every visual network analysis task we propose. We publicly release the first benchmark for the evaluation of VLMs on foundational VNA tasks.

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