RobustFT: Robust Supervised Fine-tuning for Large Language Models under
  Noisy Response
Authors: name: Junyu Luo, name: Xiao Luo, name: Kaize Ding, name: Jingyang Yuan, name: Zhiping Xiao, name: Ming Zhang

Supervised fine-tuning (SFT) plays a crucial role in adapting large language
models (LLMs) to specific domains or tasks. However, as demonstrated by
empirical experiments, the collected data inevitably contains noise in
practical applications, which poses significant challenges to model performance
on downstream tasks. Therefore, there is an urgent need for a noise-robust SFT
framework to enhance model capabilities in downstream tasks. To address this
challenge, we introduce a robust SFT framework (RobustFT) that performs noise
detection and relabeling on downstream task data. For noise identification, our
approach employs a multi-expert collaborative system with inference-enhanced
models to achieve superior noise detection. In the denoising phase, we utilize
a context-enhanced strategy, which incorporates the most relevant and confident
knowledge followed by careful assessment to generate reliable annotations.
Additionally, we introduce an effective data selection mechanism based on
response entropy, ensuring only high-quality samples are retained for
fine-tuning. Extensive experiments conducted on multiple LLMs across five
datasets demonstrate RobustFT's exceptional performance in noisy scenarios.