Conceptualizing Self-Evolution in LLMs

Self-evolution intends to replicate a form of experiential learning akin to humans, enabling LLMs to learn autonomously. Key aspects include:

• Experience acquisition, refinement, and application phases, modeled after human learning cycles.
• Overview of evolution objectives and proposed frameworks to enhance this process.

Analysis and Challenges:

• Identification and discussion of the main challenges in implementing self-evolving systems.
• Future directions include refining iterative learning processes and expanding the applicability to more complex scenarios.

Importance and Prospects:

This survey highlights a pioneering approach to making LLMs more adaptive and efficient, potentially paving the way for more advanced applications, such as in adaptive educational systems or complex problem-solving tasks.