Summary of Towards Rationality in Language and Multimodal Agents: a Survey, by Bowen Jiang et al.
Towards Rationality in Language and Multimodal Agents: A Survey
by Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Yuan Yuan, Zhuoqun Hao, Xinyi Bai, Weijie J. Su, Camillo J. Taylor, Tanwi Mallick
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
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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 paper explores the development of rational language and multimodal agents, focusing on defining criteria for rationality in intelligent systems. The authors highlight the importance of rational decision-making, which aligns with evidence and logical principles, in reliable problem-solving. They argue that current large language models (LLMs) often fall short due to their bounded knowledge space and inconsistent outputs. To overcome this limitation, researchers have shifted towards developing multimodal and multi-agent systems, integrating modules like external tools, symbolic reasoners, and utility functions. The paper surveys state-of-the-art advancements in language and multimodal agents, assesses their role in enhancing rationality, and outlines open challenges and future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rational machines are being developed to make better decisions. Right now, big language models often don’t make sense because they only know so much and can be inconsistent. To fix this, researchers are working on combining different types of intelligence, like visual and symbolic thinking. This paper looks at the latest developments in language and multimodal agents, how well they work, and what needs to happen next. |
