SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation
Authors: name: Junyu Luo, name: Xiao Luo, name: Xiusi Chen, name: Zhiping Xiao, name: Wei Ju, name: Ming Zhang

Supervised fine-tuning (SFT) is crucial in adapting large language models
(LLMs) to a specific domain or task. However, only a limited amount of labeled
data is available in practical applications, which poses a severe challenge for
SFT in yielding satisfactory results. Therefore, a data-efficient framework
that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly
anticipated. Towards this end, we introduce a semi-supervised fine-tuning
framework named SemiEvol for LLM adaptation from a propagate-and-select manner.
For knowledge propagation, SemiEvol adopts a bi-level approach, propagating
knowledge from labeled data to unlabeled data through both in-weight and
in-context methods. For knowledge selection, SemiEvol incorporates a
collaborative learning mechanism, selecting higher-quality pseudo-response
samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven
general or domain-specific datasets, demonstrating significant improvements in
model performance on target data. Furthermore, we compared SemiEvol with SFT
and self-evolution methods, highlighting its practicality in hybrid data
scenarios.