Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
Rui Wang, Fei Mi, Yi Chen, Boyang Xue, Hongru Wang, Qi Zhu, Kam-Fai Wong, Ruifeng Xu
Abstract
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.- Anthology ID:
- 2024.findings-naacl.145
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2243–2255
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.145
- DOI:
- Cite (ACL):
- Rui Wang, Fei Mi, Yi Chen, Boyang Xue, Hongru Wang, Qi Zhu, Kam-Fai Wong, and Ruifeng Xu. 2024. Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2243–2255, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models (Wang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.findings-naacl.145.pdf