FAIT introduces a novel fine-tuning approach that helps large language models identify and avoid common coding errors by focusing on error-sensitive parts of code.

• Improves functional correctness by differentially weighting tokens during model training
• Reduces the generation of plausible-looking but incorrect code by 14-38%
• Demonstrates effectiveness across multiple programming languages and code generation benchmarks
• Requires no additional training data, only modified fine-tuning techniques

This research significantly advances software engineering capabilities by improving automated code generation reliability and reducing the need for extensive debugging, making AI coding assistants more valuable for development teams.

FAIT: Fault-Aware Fine-Tuning for Better Code Generation