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Discuss Transformer LLM Design

NVIDIAMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenML System Design

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Discuss Transformer LLM Design evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Question

Explain the architecture of a Transformer-based large language model (LLM). How does self-attention enable long-range dependency modeling? Describe how you would fine-tune a pretrained LLM on a domain-specific corpus while avoiding catastrophic forgetting. How would you evaluate, monitor, and mitigate hallucinations in an LLM that serves user queries in production?