Filtering before Tuning: Robust Fine-Tuning of Large Code Models under Noisy Labels

Fine-tuning plays a crucial role in adapting large code models (LCMs) to specific software engineering tasks. However, fine-tuning LCMs requires perfectly labeled datasets, which are rarely available in practice. Noisy labels in the training data can significantly impair the generalization ability and overall performance of fine-tuned LCMs. Previous work has primarily focused on the problem of noisy labels in training models from scratch, while this problem remains largely unexplored in the context of fine-tuning LCMs.