Explain LLM architecture, tuning, evaluation
Overview
This question evaluates understanding of transformer-based LLM architectures, positional embedding variants, parameter-efficient fine-tuning (PEFT) approaches, regularization strategies, and evaluation methodologies within the Machine Learning domain focused on large language models and NLP, emphasizing both conceptual understanding and practical application. It is commonly asked to assess the interviewee’s ability to reason about architecture and tuning trade-offs, model generalization and stability, and the design of reliable offline and online evaluation pipelines for production-minded language model systems.
LLM Architecture, Positional Embeddings, Fine-Tuning (PEFT), Regularization, and Evaluation
Context
You are interviewing for a Machine Learning Engineer role with a focus on large language models. Provide a concise but technically solid overview with production-minded trade-offs.
Tasks
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Compare Transformer architectures:
- Encoder–decoder vs. decoder-only. When is each used, and why?
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Detail the decoder-only Transformer stack:
- Self-attention (causal), feed-forward/MLP blocks, normalization, and residual connections; note common design choices.
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Explain positional embeddings:
- Absolute (sinusoidal vs. learned), relative, and rotary (RoPE). Discuss trade-offs (length generalization, efficiency, stability).
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Describe hands-on fine-tuning using PEFT:
- Methods such as LoRA, adapters, prefix/prompt tuning, IA3, QLoRA. When to choose which, with practical considerations.
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Preventing overfitting during training:
- Regularization (e.g., dropout, weight decay), data augmentation, early stopping, and other tactics.
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Evaluation strategy:
- Intrinsic vs. extrinsic metrics. Offline vs. online validation, including how to run safe and informative experiments.
Solution
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