Summary of A Survey on Self-evolution Of Large Language Models, by Zhengwei Tao et al.
A Survey on Self-Evolution of Large Language Models
by Zhengwei Tao, Ting-En Lin, Xiancai Chen, Hangyu Li, Yuchuan Wu, Yongbin Li, Zhi Jin, Fei Huang, Dacheng Tao, Jingren Zhou
First submitted to arxiv on: 22 Apr 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 A comprehensive survey of self-evolution approaches in large language models (LLMs) has the potential to scale LLMs towards superintelligence. The paper proposes a conceptual framework for self-evolution, outlining an iterative process composed of experience acquisition, refinement, updating, and evaluation phases. It categorizes evolution objectives of LLMs and agents, summarizing literature and providing taxonomy and insights for each module. The authors highlight existing challenges and propose future directions to improve self-evolution frameworks. This work is particularly significant in the context of LLM applications, as it enables autonomous learning from experiences generated by the model itself, potentially leading to more efficient and effective models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made great progress in many areas! However, they can be expensive and may not always get better when we ask them to do harder tasks. A new way of training these models is emerging, where they learn from their own experiences. This could lead to even more powerful AI systems. The paper looks at different ways this self-evolution approach works, how it’s used in various applications, and what challenges need to be addressed. |
