SISA Certified Security Professional in Artificial Intelligence CSPAI Question # 6 Topic 1 Discussion
CSPAI Exam Topic 1 Question 6 Discussion:
Question #: 6
Topic #: 1
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain. What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?
A.
Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.
B.
Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine-tuning.
C.
Single-task fine-tuning requires significantly more data to achieve comparable performance to multi-task fine tuning.
D.
Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.
Single-task fine-tuning specializes the LLM but risks overfitting, limiting generalization to novel tasks unlike multi-task approaches that promote transfer learning across domains. This challenge requires careful regularization in SDLC to balance specificity and versatility, often needing more resources for version management. Exact extract: "Single-task fine-tuning is less effective in generalizing to new tasks compared to multi-task fine-tuning." (Reference: Cyber Security for AI by SISA Study Guide, Section on Fine-Tuning Challenges, Page 115-118).
Contribute your Thoughts:
Chosen Answer:
This is a voting comment (?). You can switch to a simple comment. It is better to Upvote an existing comment if you don't have anything to add.
Submit