A Survey on Self-Evolution of Large Language Models
(2404.14387)Abstract
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.
Overview
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The paper discusses the self-evolution mechanisms of LLMs using a structured framework that includes experience acquisition, refinement, updating, and evaluation.
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It breaks down the experience acquisition into task evolution, solution evolution, and feedback methods, pinpointing the importance of both task relevance and response mechanisms.
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Refinement and updating methods focus on ensuring only high-quality data is used in model enhancements, employing strategies like filtering, correcting, and context-aware parameter adjustments.
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The paper emphasizes the need for continuous evaluation and adaptation, proposing dynamic benchmarks and greater alignment with human values and safety.
Comprehensive Survey on Self-Evolution in LLMs
Introduction and Framework
The concept of self-evolution in LLMs draws inspiration from the natural learning capabilities of humans. This survey introduces a structured conceptual framework for the autonomous evolution of LLMs. The proposed model operates through iterative cycles, comprising phases like experience acquisition, refinement, updating, and evaluation, aligned with a specified evolution objective. The process remarkably mirrors human learning, with the ability of LLMs to evolve iteratively based on autonomous interactions with tasks and environments.
Experience Acquisition
Acquiring new experiences is fundamental for LLMs to evolve autonomously. The survey breaks down this process into task evolution, solution evolution, and feedback methods:
- Task Evolution: Tasks are either generated or selected based on their relevance to the current evolution objectives. Methods vary from knowledge-based (leveraging external information) to knowledge-free (relying on self-generated tasks), and selective strategies that filter from a predefined set of tasks.
- Solution Evolution: Solutions to tasks are evolved through methods categorized as positive or negative. Positive methods ensure task alignment and correctness, while negative methods, such as contrastive learning from negative examples, help in refining model outputs.
- Feedback: Feedback mechanisms, essential for assessing solution efficacy, include model-generated evaluations and direct responses from environments. This feedback is crucial for subsequent experience refinement.
Experience Refinement
Post-experience acquisition, refining these experiences enhances their quality before they are used for model updates. The refinement strategies discussed are:
- Filtering: Implementing metric-based or metric-free strategies to ensure only high-quality, reliable data is utilized for updates.
- Correcting: Employing critique-based or critique-free corrections to refine the experiences further.
Updating Methods
Updating is pivotal for incorporating refined experiences into the model. The survey analyzes in-weight updates (adjusting model parameters) and in-context updates (utilizing external and working memory), highlighting their roles in preserving knowledge continuity and facilitating rapid adaptation.
Evaluation Techniques
Evaluating the performance of evolved models is crucial for continuous improvement. The discussion here spans both quantitative and qualitative approaches, providing insights into model performance and highlighting the necessity for dynamic evaluation mechanisms to keep pace with evolving models.
Open Problems and Future Directions
Several challenges and future research directions are highlighted:
- Expanding Evolution Objectives: Current frameworks need to cover more diverse and complex objectives.
- Autonomous Levels: Increasing the autonomy level in self-evolution from low to high remains a critical task.
- Theoretical Foundations: Establishing a robust theoretical backbone for self-evolution methods, particularly in experience acquisition and refinement techniques.
- Stability-Plasticity Dilemma: Addressing the balance between retaining learned knowledge and adapting to new information.
- Systematic and Evolving Evaluation: Developing dynamic benchmarks that adapt to the evolved capabilities of LLMs.
- Safety and Alignment: Ensuring evolved models align with human values, emphasizing the importance of superalignment initiatives.
Conclusion
The survey underscores the transformative potential of self-evolving LLMs in mimicking human-like learning processes, marking a significant step toward superintelligent AI systems. By delineating the current state of research, challenges, and prospects, it lays a substantial foundation for future advancements in this burgeoning field.
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