Improving the Robustness of Large Language Models for Code Tasks via Fine-tuning with Perturbed Data

Context: In the fast-paced evolution of software development, Large Language Models (LLMs) have become indispensable tools for tasks such as code generation, completion, analysis, and bug fixing. Ensuring the robustness of these models against potential vulnerabilities from handling diverse inputs is critical, as variations in input can lead to incorrect or insecure code outputs. Objective: This work aims to improve the robustness of LLMs for coding-related tasks against potential adversarial inputs. Specifically, we investigate how fine-tuning LLMs with perturbed datasets impacts their robustness against input perturbations. Method: We systematically evaluated LLM robustness by fine-tuning models using datasets perturbed at character-level, word-level, and sentence-level, comparing results against base models and models fine-tuned on unperturbed datasets. Results: Fine-tuning LLMs with perturbed datasets significantly improves model robustness (RD usually drops around 4% - 6%), especially for models with relatively weak robustness. However, this fine-tuning process typically results in a slight performance decrease (pass@1 usually drops around 1% - 3%) compared to fine-tuning with unperturbed datasets, although occasional performance improvements are observed. Conclusion & Implications: Fine-tuning LLMs for coding tasks with perturbed data effectively enhances their robustness at the cost of a minor performance reduction, emphasizing the importance of balancing the robustness and performance of LLMs for coding applications.