Explain LLM tuning and transformer basics

Q: Explain LLM tuning and transformer basics

This question evaluates competencies in large language model tuning and Transformer fundamentals, covering fine-tuning strategies, dataset construction and labeling, model adaptation choices, loss functions and evaluation metrics, regularization techniques, optimizer selection, self-attention and multi-head attention, and the end-to-end Transformer decoder mathematical steps. It is commonly asked in Machine Learning interviews because it probes both conceptual understanding and practical application—examining architectural trade-offs, optimization and regularization decisions, deployment constraints, and reasoning about failure modes such as overfitting and hallucination within the Machine Learning domain.

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Question

Answer the following machine learning questions:

Describe a project where you fine-tuned a large language model or another large foundation model. Explain the task, dataset construction, labeling strategy, model adaptation method (full fine-tuning vs. parameter-efficient tuning), loss function, evaluation metrics, deployment constraints, and what you would do if the model overfits or hallucinates.
Explain regularization and compare common forms such as L1, L2/weight decay, dropout, early stopping, and data augmentation.
Compare common optimizers such as SGD, Momentum, Adam, and AdamW, including when you would choose each.
Explain self-attention and multi-head attention, including the core equations.
Describe the Transformer architecture end-to-end and write the main mathematical steps used in a decoder-style Transformer block.

Explain LLM tuning and transformer basics

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