Summary of Humaneval-v: Benchmarking High-level Visual Reasoning with Complex Diagrams in Coding Tasks, by Fengji Zhang et al.
HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks
by Fengji Zhang, Linquan Wu, Huiyu Bai, Guancheng Lin, Xiao Li, Xiao Yu, Yue Wang, Bei Chen, Jacky Keung
First submitted to arxiv on: 16 Oct 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 research paper presents HumanEval-V, a comprehensive benchmark for evaluating Large Multimodal Models’ (LMMs) diagram interpretation and reasoning abilities in coding contexts. The benchmark consists of six task types featuring carefully crafted diagrams with function signatures and test cases, assessing models’ comprehension through novel code generation tasks. The study finds that even top-performing LMMs achieve modest success rates, highlighting substantial room for improvement. Analysis reveals that current LMMs struggle with spatial transformations, topological relationships, and dynamic patterns that humans find intuitive, providing valuable insights for advancing LMMs’ visual reasoning abilities. The paper’s findings have implications for the development of more accurate and efficient LMMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how well computers can understand diagrams and make decisions based on them. Currently, computers are not very good at this task, even though they’re great at other things like recognizing images or understanding language. The researchers created a special test to see how well computers do with diagrams in coding contexts. They found that most computers don’t do very well, especially when it comes to understanding spatial relationships and patterns. This means we need to improve computer learning systems so they can better understand diagrams and make more accurate decisions. |
