Key Points
1. Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. Self-evolution approaches that enable LLM to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing, offering the potential to scale LLMs towards superintelligence.
2. Rapid development in artificial intelligence has led to the emergence of large language models (LLMs) like GPT-3.5, GPT-4, Gemini, LLaMA, and Qwen, marking a significant shift in language understanding and generation.
3. Current LLMs encounter challenges in complex tasks due to inherent difficulties in modeling, annotation, and evaluation associated with existing training paradigms.
4. Research on the self-evolution of LLMs has rapidly increased at different stages of model development, such as self-instruct, self-play, self-improving, and self-training.
5. LLMs can self-evolve by acquiring new tasks aligned with evolution objectives, refining these experiences to obtain better supervision signals, updating the model in-weight or in-context, and evaluating its progress before moving to the next cycle.
6. Self-evolving LLMs are capable of transcending the limitations of current static, data-bound models and mark a shift towards more dynamic, robust, and intelligent systems.
7. Two primary categories of self-evolution approaches are critique-based and critique-free correction methods, with the former employing critiques to guide the model towards improved iterations and the latter correcting experiences based on objective information.
8. Updating methods for LLMs include in-weight learning, involving updates to model weights, and in-context learning, involving updates to external or working memory.
9. External memory, as an approach to in-context learning, utilizes an external module to collect, update, and retrieve past experiences and knowledge, enabling models to access a rich pool of insights and achieve better results. The operations associated with updating external memory include insert, reflection, and forget.
Summary
In the paper "A Survey on Self-Evolution of Large Language Models," the authors provide a comprehensive overview of the advancements and applications of large language models (LLMs) in various domains. The paper emphasizes the critical role of self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself. The self-evolution methods aim to address challenges such as task complexity, diversity, and the limitations of existing training paradigms.
The paper categorizes self-evolution approaches into four phases: experience acquisition, experience refinement, updating, and evaluation. The methods include self-instruct, self-play, self-improving, and self-training, which enable LLMs to adapt, learn, and improve autonomously. These approaches leverage both external knowledge and experiences generated by the model itself to guide the process of self-evolution.
Advancements in Large Language Models
The advancements in large language models include achievements in areas such as question answering, mathematical reasoning, code generation, and task-solving that require interaction with environments. The paper also explores the concept of self-evolution in artificial intelligence, drawing parallels to the human experiential learning process and discussing the philosophical implications of AI's capacity for self-evolution.
Framework and Objectives for Self-Evolution
The survey outlines the framework for self-evolution, emphasizing the iterative cycle of learning and improvement. It also categorizes the evolution objectives and methods in self-evolving LLMs, providing insights into the evolving abilities and evolution directions.
The paper details various self-evolution frameworks and next-generation models, showcasing the potential for significant advances in the development of self-evolving large language models. The research aims to pave the way for the future development of LLMs equipped with critical insights to fast-track their self-evolution.
Integration of Past Experiences in Self-Evolving LLMs
The paper provides a comprehensive overview of large language models (LLMs) and their applications in various fields, focusing on their advancements and the challenges inherent in developing self-evolving LLMs. The paper emphasizes the importance of integrating past experiences and knowledge into the agents' memory stream to refine their state or beliefs for improved performance and adaptability across various tasks and environments.
Evaluation Process for Evolved Models
The paper outlines the evaluation process for evolved models, categorizing it into quantitative and qualitative approaches that provide valuable insights into model performance and areas for improvement. It discusses the use of LLMs as human proxies for automatic evaluators, highlighting the development of reward model scores and the use of LLMs to evaluate other LLMs. Additionally, it introduces the concept of evolving objectives with potential hierarchical structures, emphasizing the need to develop self-evolution frameworks that can comprehensively address diverse and hierarchical objectives.
Challenges and Unresolved Issues
The paper also addresses the challenges and unresolved issues in self-evolving LLMs, such as the stability-plasticity dilemma, the use of self-generated data for learning, and the need for a dynamic, comprehensive benchmark. It further discusses the importance of ensuring that LLMs align with human values and preferences to mitigate biases and suggests the development of scalable training methods to align LLMs with human ethics and values.
In conclusion, the paper provides a comprehensive framework for understanding and developing self-evolving LLMs, highlighting their potential to adapt, learn, and improve autonomously. It also identifies existing challenges and proposes directions for future research to accelerate progress toward more dynamic, intelligent, and efficient models in AI.
Reference: https://arxiv.org/abs/2404.143...