Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs

arXiv:2405.02774v1 Announce Type: new
Abstract: This work focuses on leveraging and selecting from vast, unlabeled, open data to pre-fine-tune a pre-trained language model. The goal is to minimize the need for costly domain-specific data for subsequent fine-tuning while achieving desired performance levels. While many data selection algorithms have been designed for small-scale applications, rendering them unsuitable for our context, some emerging methods do cater to language data scales. However, they often prioritize data that aligns with the target distribution. While …

arxiv cs.ai cs.cl cs.lg data fine-tuning llms type