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
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Publisher:
Association for Computational Linguistics
Note:
Pages:
2243–2255
Language:
URL:
https://aclanthology.org/2024.findings-naacl.145
DOI:
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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)
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