A Survey on Self-Evolution of Large Language Models

arXiv:2404.14387v1 Announce Type: new
Abstract: 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 …

abstract advanced agent applications arxiv complexity cs.ai cs.cl current diversity evolution face fields however human intelligent issue language language models large language large language models learn llm llms performance supervision survey type