Summary of Kiva: Kid-inspired Visual Analogies For Testing Large Multimodal Models, by Eunice Yiu et al.
KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models
by Eunice Yiu, Maan Qraitem, Anisa Noor Majhi, Charlie Wong, Yutong Bai, Shiry Ginosar, Alison Gopnik, Kate Saenko
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores visual analogical reasoning in large multimodal models (LMMs) by comparing their performance to human adults and children. The researchers propose a new benchmark, comprising 4,300 visual transformations of everyday objects, to evaluate LLMs’ ability to reason analogically and apply rules to new scenarios. The evaluation consists of three stages: identifying what changed, how it changed, and applying the rule to new objects. Results show that while LMMs excel in identifying “what” changes, they struggle with quantifying “how” changes occurred and extrapolating rules to new objects. In contrast, children and adults exhibit stronger analogical reasoning abilities across all stages. Notably, GPT-o1, a strong-performing model, excels in tasks involving simple visual attributes like color and size, while struggling with more complex tasks requiring extensive cognitive processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how well computers can solve problems that require thinking ahead. The researchers created a new test to see if these computer models can learn from what they see and apply rules to new situations. They compared the models’ performance to children aged 3-5 and adults, who are all good at solving this type of problem. The results show that while computers are great at recognizing what’s different about two pictures, they struggle to understand how something changed or apply those changes to new situations. Children and adults, on the other hand, can do these tasks much better. This study highlights the limitations of training computer models solely with 2D images and text. |
Keywords
» Artificial intelligence » Gpt
