Summary of M3gia: a Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark, by Wei Song et al.
M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark
by Wei Song, Yadong Li, Jianhua Xu, Guowei Wu, Lingfeng Ming, Kexin Yi, Weihua Luo, Houyi Li, Yi Du, Fangda Guo, Kaicheng Yu
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Recent advancements in multi-modality large language models (MLLMs) have led to impressive task performance on complex tasks. However, evaluating their general intelligence beyond superficial achievements remains unexplored. This paper introduces M3GIA, a novel cognitive-driven benchmark for assessing the intelligence of MLLMs across languages, including Chinese, French, Spanish, Portuguese, and Korean. The benchmark is designed to evaluate five key cognitive factors based on the Cattell-Horn-Carrol (CHC) model of intelligence. A comprehensive corpus of data from human participants reveals that the most advanced MLLM reaches the lower boundary of human intelligence in English, but there remains a disparity across languages. This study also uncovers an interesting “winner-takes-all” phenomenon aligned with cognitive studies. The benchmark will be open-sourced to facilitate the enhancement of cognitive capabilities in MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how good large language models are at doing things, but it’s not just about being good at one task. It wants to know if these models can think like humans do. To find out, researchers created a new way to test the models’ thinking abilities across different languages. They tested the most advanced model and found that it’s almost as smart as a human in English, but it’s not as good in other languages. This study is important because it helps us understand how these language models work and how we can make them even smarter. |
