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?
Quick Answer: 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.
hardMachine Learning EngineerTechnical ScreenML System Design
38
0
Discuss Transformer LLM Design
System-Design-Oriented LLM Question
Context: You are designing, fine-tuning, and operating a Transformer-based large language model (LLM) that answers user queries in production. Address model architecture, training strategy, and operational safeguards.
Tasks
Architecture of a Transformer-based LLM
Describe the core components of a decoder-only Transformer used in modern LLMs (tokenization, embeddings, positional encodings, attention/MLP blocks, normalization, residuals, training objective, inference optimizations).
How self-attention enables long-range dependency modeling
Explain the scaled dot-product self-attention mechanism and why it captures long-range dependencies better than RNNs/CNNs. Note limits and common long-context enhancements.
Fine-tuning a pretrained LLM on a domain-specific corpus while avoiding catastrophic forgetting
Propose a practical, step-by-step fine-tuning plan (data curation, method choice, hyperparameters, regularization) that preserves general capabilities.
Evaluate, monitor, and mitigate hallucinations for a production LLM