In the world of artificial intelligence, language models (LLMs) have been gaining popularity as a powerful tool for generating human-like text. However, these models are not born with innate knowledge but must be trained on vast amounts of data to learn and improve. In this study, we explore the potential of "self-evolving" LLMs that can independently refine their responses through a process similar to biological evolution. We compare the performance of two training methods: multiple self-refinement and single self-refinement, and analyze their impact on model performance.
Multiple Self-Refinement (DF R−multi)
The first method we explore is called "multiple self-refinement" or DF R−multi. In this approach, the LLM generates multiple responses to a given prompt, and then selects the best one for further refinement. This process is repeated multiple times until the model produces an optimal response. The idea behind this method is that by generating multiple responses, the model can explore different possibilities and arrive at a better solution through trial and error.
Single Self-Refinement (DF R)
The second method we examine is "single self-refinement" or DF R. In this approach, the LLM generates only one response to a given prompt and then refines it based on its performance. This process is repeated until the model produces an optimal response. The advantage of this method is that it requires fewer computational resources than multiple self-refinement and can lead to faster convergence.
Comparison of Methods
We evaluate the performance of both methods using meticulously crafted datasets and reinforcement learning-based methods (Ouyang et al., 2022). Our results show that while both methods can improve model performance, single self-refinement (DF R) leads to better outcomes in terms of response quality and diversity. Additionally, we find that the two methods have different strengths and weaknesses, with multiple self-refinement (DF R−multi) excelling in exploring new possibilities and single self-refinement (DF R) performing better in converging to a single optimal response.
Conclusion
In conclusion, our study demonstrates the potential of "self-evolving" LLMs that can independently refine their responses through a process similar to biological evolution. By comparing two training methods, we show that both have their advantages and disadvantages but lead to better outcomes in terms of response quality and diversity. These findings pave the way for further research into the autonomous evolution of LLMs and their potential applications in various domains.
