Summary of Exploring the Reasoning Abilities Of Multimodal Large Language Models (mllms): a Comprehensive Survey on Emerging Trends in Multimodal Reasoning, by Yiqi Wang et al.
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
by Yiqi Wang, Wentao Chen, Xiaotian Han, Xudong Lin, Haiteng Zhao, Yongfei Liu, Bohan Zhai, Jianbo Yuan, Quanzeng You, Hongxia Yang
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 surveys the current state of Multimodal Large Language Models (MLLMs) in achieving Artificial General Intelligence (AGI) with abstract reasoning ability. Recent advancements in LLMs and MLLMs have demonstrated impressive capabilities across various tasks and applications, but the reasoning abilities of MLLMs have not been systematically investigated. The paper comprehensively reviews existing evaluation protocols for multimodal reasoning, categorizes and illustrates the frontiers of MLLMs, and discusses recent trends and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial General Intelligence (AGI) is a goal for next-generation AI. Large Language Models (LLLMs) have shown impressive capabilities across many tasks. But what can these models really do? This paper looks at how well they can reason, which is important for making decisions and solving problems. The study reviews how experts test LLMs’ reasoning abilities and talks about the frontiers of this technology. It also discusses recent developments in using these models to solve complex problems. |
