Summary of Core Knowledge Deficits in Multi-modal Language Models, by Yijiang Li et al.
Core Knowledge Deficits in Multi-Modal Language Models
by Yijiang Li, Qingying Gao, Tianwei Zhao, Bingyang Wang, Haoran Sun, Haiyun Lyu, Dezhi Luo, Hokin Deng
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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 research paper investigates the limitations of Multimodal Large Language Models (MLLMs) in performing simple tasks that are intuitive for humans. The authors propose that these deficiencies stem from the absence of core knowledge, which is innate to humans from early childhood. To test this hypothesis, they develop a large-scale benchmark called CoreCognition dataset, comprising 12 core cognitive concepts. They evaluate 219 models with 10 different prompts, resulting in a total of 2409 data points for analysis. The findings reveal core knowledge deficits in early developed core abilities while models demonstrate human comparable performance in high-level cognition. Additionally, the study introduces an evaluation technique called Concept Hacking, which shows that MLLMs do not genuinely advance toward core knowledge but instead rely on illusory understanding and shortcut learning as they scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores why large language models are good at some things, like reasoning, but struggle with simple tasks that humans find easy. The scientists think this might be because the models lack basic knowledge, which we learn from childhood onwards. To test this idea, they created a special dataset with 12 key ideas and tested many different models on it. They found that while the models are great at complex thinking, they’re not as good at simple tasks. The study also shows how these models “cheat” by relying on shortcuts rather than truly understanding the concepts. |
