A Survey on Self-Evolution of Large Language Models
arxiv(2024)
摘要
Large language models (LLMs) have significantly advanced in various fields
and intelligent agent applications. However, current LLMs that learn from human
or external model supervision are costly and may face performance ceilings as
task complexity and diversity increase. To address this issue, self-evolution
approaches that enable LLM to autonomously acquire, refine, and learn from
experiences generated by the model itself are rapidly growing. This new
training paradigm inspired by the human experiential learning process offers
the potential to scale LLMs towards superintelligence. In this work, we present
a comprehensive survey of self-evolution approaches in LLMs. We first propose a
conceptual framework for self-evolution and outline the evolving process as
iterative cycles composed of four phases: experience acquisition, experience
refinement, updating, and evaluation. Second, we categorize the evolution
objectives of LLMs and LLM-based agents; then, we summarize the literature and
provide taxonomy and insights for each module. Lastly, we pinpoint existing
challenges and propose future directions to improve self-evolution frameworks,
equipping researchers with critical insights to fast-track the development of
self-evolving LLMs.
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