Summary of Symbolic Learning Enables Self-evolving Agents, by Wangchunshu Zhou et al.
Symbolic Learning Enables Self-Evolving Agents
by Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, Huajun Chen, Yuchen Eleanor Jiang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper explores a pathway to artificial general intelligence (AGI) through developing “language agents”, complex large language models (LLMs) pipelines involving prompting techniques and tool usage methods. Current research on language agents is model-centric, requiring substantial manual engineering efforts from human experts. The authors argue that this limitation can be overcome by transitioning from model-centric to data-centric approaches, allowing language agents to autonomously learn and evolve in environments. This would enable the possibility of achieving AGI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to make artificial intelligence (AI) more powerful. They call it “language agents” which are special kinds of AI models that can understand and generate human-like language. Right now, making these language agents requires humans to manually engineer them, but the authors think this limitation can be overcome by letting the language agents learn from data on their own. This could lead to a more powerful kind of AI called artificial general intelligence (AGI). |
Keywords
» Artificial intelligence » Prompting
