Discuss Transformer LLM Design

Q: Discuss Transformer LLM Design

This question evaluates proficiency in Transformer-based LLM architecture, attention mechanisms, domain-specific fine-tuning, and production safeguards for hallucination detection and mitigation.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Question

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

Describe offline evaluation, online monitoring, and mitigation techniques (e.g., retrieval augmentation, verification, constrained decoding, confidence calibration, human-in-the-loop).

Discuss Transformer LLM Design

System-Design-Oriented LLM Question

Tasks

Solution

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