Explain LLM architecture, tuning, evaluation
AmazonApplied Scientist · Technical Screen
Last updated
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.
What is the architecture of large language models? Compare encoder–decoder and decoder-only Transformers, detailing the decoder-only stack (self-attention, feed-forward blocks, normalization, and residual connections). How are positional embeddings handled (absolute, relative, rotary), and what trade-offs exist? Describe your hands-on experience fine-tuning LLMs, including PEFT methods (e.g., LoRA, adapters) and when to choose each. During training, how do you prevent overfitting (regularization, data augmentation, early stopping), and how do you evaluate the model (intrinsic/extrinsic metrics, offline vs. online validation)?
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