Dial-insight: Fine-tuning Large Language Models with High-Quality
  Domain-Specific Data Preventing Capability Collapse
Jianwei Sun Chaoyang Mei Linlin Wei Kaiyu Zheng Na Liu Ming Cui Tianyi Li
The efficacy of large language models (LLMs) is heavily dependent on the
quality of the underlying data, particularly within specialized domains. A
common challenge when fine-tuning LLMs for domain-specific applications is the
potential degradation of the model's generalization capabilities. To address
these issues, we propose a two-stage approach for the construction of
production prompts designed to yield high-quality data. This method involves
the generation of a diverse array of prompts that encompass a broad spectrum of
tasks and exhibit a rich variety of expressions. Furthermore, we introduce a
cost-effective, multi-dimensional quality assessment framework to ensure the
integrity of the generated labeling data. Utilizing a dataset comprised of
service provider and customer interactions from the real estate sector, we
demonstrate a positive correlation between data quality and model performance.
Notably, our findings indicate that the domain-specific proficiency of general
LLMs can be enhanced through fine-tuning with data produced via our proposed
method, without compromising their overall generalization abilities, even when
exclusively domain-specific data is employed for fine-tuning.