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SISA CSPAI Exam - Topic 5 Question 14 Discussion

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?
B) Single-task fine-tuning is less effective in generalizing to new, unseen tasks 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.
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.

SISA CSPAI Exam - Topic 5 Question 14 Discussion

Actual exam question for SISA's CSPAI exam
Question #: 14
Topic #: 5
[All CSPAI Questions]

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?

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Suggested Answer: B

by Noel at May 02, 2026, 12:24 AM

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Marvel
5 hours ago
I'm not entirely sure, but I feel like managing versions could be a challenge, which makes option A seem plausible too.
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Bernardo
5 days ago
I think I read somewhere that single-task fine-tuning might struggle with generalization, so option B could be the right choice.
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Vanda
11 days ago
I remember discussing how single-task fine-tuning can lead to overfitting, which might relate to option D.
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Lenita
16 days ago
I feel like managing versions could be a challenge, but I’m not convinced that’s the primary issue. It seems more about performance and generalization.
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Kristal
2 months ago
I practiced a question similar to this, and I think single-task fine-tuning does require more data, which might support option C.
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Lanie
2 months ago
I think I read somewhere that single-task fine-tuning can struggle with generalization, so option B sounds plausible, but I'm not entirely sure.
upvoted 0 times
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Kallie
2 months ago
I remember discussing how single-task fine-tuning might lead to overfitting, which could relate to option D about degrading performance on original tasks.
upvoted 0 times
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