To empower LLM for security purposes, a series of optimizations and adaptability measures need to be undertaken to ensure the model is suited for the specific domain of program analysis:
(1) Fine-tuning: Further train the model using a dataset specific to the task.
(2) Transfer Learning: Transfer learning is closely related to fine-tuning but is a more general concept that can be achieved in various ways, including fine-tuning. Besides fine-tuning, transfer learning encompasses other techniques such as feature extraction, model fusion, domain adaptation, etc. In some cases, transfer learning may involve using data from different domains to improve the model's generalization performance, not just fine-tuning on a specific task.
(3) Prompt Engineering: Adjusting the input prompts to guide LLM in generating specific types of responses.
(4) Sampling: Adjusting temperature parameters, generating results multiple times, and selecting results through voting.
(5) Chain of Thoughts:
(4) Architecture Adjustment: Modifying LLM's architecture, including adding additional layers or invoking specific tools outside of LLM.
Analysis of Optimization Methods
Advantages of methods like fine-tuning:
- Stronger task adaptability for specific program analysis tasks.
- Relatively straightforward, requiring only well-annotated datasets for training.
Disadvantages of methods like fine-tuning:
- Fine-tuning typically requires relatively large labeled datasets.
- It may necessitate significant computational resources, especially when using large pre-trained models.
Advantages of prompt engineering methods:
- Flexibility - large models inherently contain a wealth of information, allowing for the design of different prompts to guide the model in generating various outputs.
- No need for labeled data.
Disadvantages of prompt engineering:
- Requires strong domain knowledge, and prompt design may require experimentation.
- Performance may not be as good as fine-tuning.