Authors: Deepanway Ghosal, Vernon Toh Yan Han, Chia Yew Ken, Soujanya Poria

Published on: March 06, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.03864

Summary

• What is new: Introduction of the novel task of multimodal puzzle solving and the dataset AlgoPuzzleVQA for evaluating multimodal language models on visual and algorithmic problem-solving.
• Why this is important: The gap between visual data interpretation and algorithmic problem solving in multimodal language models.
• What the research proposes: A new dataset, AlgoPuzzleVQA, consisting of puzzles that require understanding of visuals, language, and algorithms.
• Results: Large language models like GPT4V and Gemini show near-random performance on the puzzle-solving tasks, highlighting the challenge of integrating visual, language, and algorithmic knowledge.

Technical Details

Technological frameworks used: Multimodal language models

Models used: GPT4V, Gemini

Data used: AlgoPuzzleVQA

Potential Impact

Educational technology, AI research organizations, and puzzle-based learning platforms

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