Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
Rui Wang, Fei Mi, Yi Chen, Boyang Xue, Hongru Wang, Qi Xin Zhu, Kam‐Fai Wong, Ruifeng Xu · 2024
The growing interest in Large Language Models (LLMs) for specialized applications has revealed a significant challenge: when tailored to specific domains, LLMs tend to experience catastrophic forgetting, compromising their general capabilities and leading to a suboptimal user experience.Additionally, crafting a versatile model for multiple domains simultaneously often results in a decline in overall performance due to confusion between domains.In response to these issues, we present the RolE Prompting Guided Multi-Domain Adaptation (REGA) strategy.This novel approach effectively manages multi-domain LLM adaptation through three key components: 1) Self-Distillation constructs and replays general-domain exemplars to alleviate catastrophic forgetting.2) Role Prompting assigns a central prompt to the general domain and a unique role prompt to each specific domain to minimize inter-domain confusion during training.3) Role Integration reuses and integrates a small portion of domainspecific data to the general-domain data, which are trained under the guidance of the central prompt.The central prompt is used for a streamlined inference process, removing the necessity to switch prompts for different domains.Empirical results demonstrate that REGA effectively alleviates catastrophic forgetting and inter-domain confusion.This leads to improved domain-specific performance compared to standard fine-tuned models, while still preserving robust general capabilities.