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.

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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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Discuss Transformer LLM Design

Overview

System-Design-Oriented LLM Question

Tasks

Solution

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