Deepseek has released an innovative new paper that focuses on large language models (LLMs) with sparse architecture, proposing a brand-new method for expert specialization fine-tuning.
In this eye-catching paper, the research team introduces a unique method called "Expert Specialized Fine-Tuning" (ESFT). This method is primarily aimed at large language models with a mixture of experts (MoE) architecture, aiming to achieve a more efficient fine-tuning process. The core idea is to identify and fine-tune only the experts most relevant to a specific task, while freezing other experts and modules within the model. This design strategy has significant advantages in many aspects.
Firstly, this method greatly improves the efficiency of fine-tuning. In traditional full-parameter fine-tuning, all parameters of the entire model need to be adjusted, which often requires a large amount of computing resources and time. ESFT, by focusing on experts relevant to specific tasks, avoids unnecessary adjustments to irrelevant parts, thus significantly reducing computational load and training time. This is of vital importance for environments with limited resources or applications that require rapid iteration.
Secondly, the ESFT method demonstrates excellent performance on downstream tasks. Surprisingly, it not only achieves performance levels comparable to full-parameter fine-tuning but also surpasses it in many cases. This means that through this expert-specialized fine-tuning approach, the model can better adapt to specific downstream tasks, fully leverage its potential, and provide more powerful language processing capabilities for various practical applications.
In summary, the new paper by Deepseek proposing the expert specialized fine-tuning method provides a new perspective and solution for the fine-tuning of large language models with sparse architecture, and is expected to attract widespread attention and application in the field of natural language processing.