Summary of Logicvista: Multimodal Llm Logical Reasoning Benchmark in Visual Contexts, by Yijia Xiao et al.
LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts
by Yijia Xiao, Edward Sun, Tianyu Liu, Wei Wang
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 proposes a benchmark called LogicVista to evaluate the logical reasoning capabilities of multimodal large language models (MLLMs) in visual contexts. The authors highlight the importance of evaluating MLLMs’ proficiency in logical reasoning tasks, which are crucial for activities like navigation and puzzle-solving. They develop 5 logical reasoning tasks that cover 9 different capabilities and use a sample of 448 multiple-choice questions to test the models’ abilities. Each question is annotated with the correct answer and human-written reasoning, allowing for both open-ended and multiple-choice evaluation. The authors comprehensively evaluate 8 MLLMs using LogicVista and make their code and data available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LogicVista is a new way to measure how well AI models can reason logically. These models are great at doing things like understanding pictures and answering questions, but they’re not very good at logical thinking. The people behind LogicVista want to change that by creating a set of tests that check if the models can solve problems in a logical way. They did this by making up 5 different kinds of tasks that require logical thinking, and then tested 8 AI models on these tasks. This helps us understand how well AI models can think logically, which is important for things like navigation and puzzle-solving. |
