Compare preference alignment methods for LLMs

Q: Compare preference alignment methods for LLMs

This question evaluates expertise in preference alignment techniques for large language models—including supervised fine-tuning, RLHF-style reward-model plus policy optimization, direct preference optimization, and AI feedback/constitutional-style approaches—and the ability to measure alignment quality across helpfulness, harmlessness, honesty, and instruction-following. It is commonly asked in Machine Learning interviews because it assesses both conceptual understanding and practical application of trade-offs, safety considerations, and evaluation strategies when selecting and validating alignment methods.

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Question

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Question

You’re asked to discuss preference alignment approaches for large language models.

Task

Compare several alignment methods and explain when you would choose each. Include pros/cons and practical considerations.

Topics to include (at minimum)

Supervised fine-tuning (SFT)
RLHF-style methods (reward model + policy optimization)
Direct preference optimization-style methods (pairwise preference optimization without explicit RL)
Using AI feedback (RLAIF) / constitutional-style approaches

Evaluation

How do you measure alignment quality and detect regressions (helpfulness, harmlessness, honesty, and instruction-following)?

Compare preference alignment methods for LLMs

Overview

Question

Task

Topics to include (at minimum)

Evaluation

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