Fine-tuning in the context of a Large Language Model (LLM) is the process of taking a pre-trained model and continuing its training on a smaller, task-specific dataset to adapt it for a particular domain, task, or tone. This allows the model to specialize and perform better on specific use cases than it would with general training alone.
Fine-tuning an LLM for code analysis involves several challenges:
- Data Quality: High-quality, labeled code datasets (e.g. with bug annotations or explanations) are scarce.
- Complex Semantics: Code requires deep understanding of syntax, logic, and execution context, which is harder to capture than natural language.
- Token Limitations: Long code files may exceed the model’s context window, limiting effectiveness.
- Overfitting: Small, domain-specific datasets can lead to overfitting and reduced generalizability.
- Evaluation Difficulty: Measuring success in code analysis (e.g. correctness, security issues) is non-trivial and often requires expert validation.