Explain LLM fine-tuning and generative models

Q: Explain LLM fine-tuning and generative models

This question evaluates understanding of LLM adaptation techniques and trade-offs (fine-tuning and parameter-efficient methods) alongside knowledge of generative model families (AE, VAE, VQ‑VAE), covering objectives, training behavior, typical failure modes, and considerations like limited data, latency/cost, domain adaptation, and safety.

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Question

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Machine Learning fundamentals (LLM / Generative AI track)

You are interviewed for an ML role focused on LLMs and generative AI.

Part A — LLM fine-tuning

What are common ways to adapt/fine-tune a pretrained LLM for a downstream task?
For each approach, explain how it works , pros/cons , and when you would choose it .
Discuss practical scenario considerations such as:
- limited labeled data
- strict latency/cost constraints
- need for domain adaptation without forgetting general capabilities
- safety/alignment requirements

Part B — Generative models

Explain and compare:

Autoencoders (AE)
Variational Autoencoders (VAE)
Vector-Quantized VAE (VQ-VAE)

For each, cover the objective, training behavior, typical failure modes, and common use cases.

Explain LLM fine-tuning and generative models

Quick Overview

Machine Learning fundamentals (LLM / Generative AI track)

Part A — LLM fine-tuning

Part B — Generative models

Solution

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