Summary of Large Action Models: From Inception to Implementation, by Lu Wang et al.
Large Action Models: From Inception to Implementation
by Lu Wang, Fangkai Yang, Chaoyun Zhang, Junting Lu, Jiaxu Qian, Shilin He, Pu Zhao, Bo Qiao, Ray Huang, Si Qin, Qisheng Su, Jiayi Ye, Yudi Zhang, Jian-Guang Lou, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this research paper, the authors aim to bridge the gap between traditional Large Language Models (LLMs) and the need for intelligent agents capable of performing real-world actions. By transitioning from LLMs to Large Action Models (LAMs), designed for action generation and execution within dynamic environments, the study enables agent systems that can transform AI from passive language understanding to active task completion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new type of artificial intelligence system that can perform real-world actions. Currently, AI is only good at understanding written or spoken language, but this research aims to make it capable of taking action in the world. This could be a big step towards making AI more like humans and enabling it to complete tasks independently. |
Keywords
» Artificial intelligence » Language understanding
