Summary of Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning, by Deepanway Ghosal et al.
Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning
by Deepanway Ghosal, Vernon Toh Yan Han, Chia Yew Ken, Soujanya Poria
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a novel task called multimodal puzzle solving, which combines visual and linguistic understanding with complex algorithmic reasoning. To evaluate the capabilities of multimodal language models in this domain, the authors create a dataset called AlgoPuzzleVQA that includes puzzles covering topics like boolean logic, combinatorics, graph theory, and optimization. The dataset is generated automatically from human-authored code and features exact solutions that can be found using algorithms, making it scalable to arbitrary complexity. The study finds that large language models (LLMs) such as GPT4V and Gemini struggle with puzzle-solving tasks, performing near-randomly in a multi-choice question-answering setup for many puzzles. This highlights the challenges of integrating visual, linguistic, and algorithmic knowledge for solving complex reasoning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about testing how well computers can solve puzzles that require both looking at pictures and understanding words. The authors created a special set of puzzles called AlgoPuzzleVQA to see how good computers are at this task. They designed the puzzles to cover different math and computer science topics, like logic and graph theory. The cool thing about these puzzles is that they have exact answers that can be found using algorithms, so it’s easy to make more puzzles by just generating code. When the authors tested popular computer language models on these puzzles, they found out that even the best ones don’t do very well – they’re almost as good as flipping a coin! This shows how hard it is for computers to combine looking at pictures and understanding words with solving complex problems. |
Keywords
» Artificial intelligence » Gemini » Optimization » Question answering
