  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
domain-specific 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.
