Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

Song Wang, Zhen Tan, Ruocheng Guo, Jundong Li

Submission Type: Regular Long Paper

Submission Track: Machine Learning for NLP

Submission Track 2: Theme Track: Large Language Models and the Future of NLP

Keywords: Pretrained Language Models, Large-scale Language Models, Learning from Noisy Labels

Abstract: Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing. However, in real-world scenarios, data labels are often noisy due to the complex annotation process, making it essential to develop strategies for fine-tuning PLMs with such noisy labels. To this end, we introduce an innovative approach for fine-tuning PLMs using noisy labels, which incorporates the guidance of Large Language Models (LLMs) like ChatGPT. This guidance assists in accurately distinguishing between clean and noisy samples and provides supplementary information beyond the noisy labels, thereby boosting the learning process during fine-tuning PLMs. Extensive experiments on synthetic and real-world noisy datasets further demonstrate the superior advantages of our framework over the state-of-the-art baselines.

Submission Number: 1309