Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud

• URL: http://arxiv.org/abs/2412.04871v1
• Date: Fri, 06 Dec 2024 09:04:12 GMT
• Title: Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud
• Authors: Yuanhao Yue, Chengyu Wang, Jun Huang, Peng Wang,
• Abstract summary: We present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs. Experiments and an application study prove the effectiveness of our approach.
• Score: 12.651588927599441
• License:
• Abstract: Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach.

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