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Fine-Tuning LLM MCQ
10 Conceptual Questions with In-Depth Technical Explanations
Compare Full Fine-Tuning, Parameter-Efficient Fine-Tuning (PEFT), LoRA (low-rank adaptation), QLoRA, and RLHF.
Fine-Tuning LLM Questions
Select an answer to reveal detailed technical explanation and formulas.
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What is LoRA (Low-Rank Adaptation, Hu et al., Microsoft 2021)?
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What is QLoRA (Dettmers et al., 2023)?
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In LoRA, what is the role of the 'lora_alpha' scaling hyperparameter?
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Can LoRA adapter weights be merged back into the original base model weights for zero-latency inference?
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What is Direct Preference Optimization (DPO, Rafailov et al., 2023), and how does it improve upon traditional RLHF?
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What is 'Prefix Tuning' (Li & Liang, 2021) in parameter-efficient fine-tuning?
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What format is standard for Supervised Fine-Tuning (SFT) datasets for instruction-following models?
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What is 'Instruction Tuning' versus domain-specific continuing pre-training?
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What is the 'Alpaca' recipe (Taori et al., Stanford 2023) for low-cost instruction tuning?
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About Fine-Tuning LLM Interview Questions & Quiz
These Fine-Tuning LLM multiple choice questions are curated to prepare engineers, researchers, and data science students for top technical interviews, machine learning certifications, and university exams.
Every MCQ incorporates core mathematical formulas, architectural tradeoffs, and practical algorithmic considerations. Practicing these questions guarantees a solid foundational and conceptual mastery of Fine-Tuning LLM.