WalmartLabs LLM Fundamentals Interview (Randomness)

This question tests your understanding of randomness and nondeterminism in Large Language Models (LLMs) across both training and inference.

You’ll be asked to explain and reason about core sources of randomness: data sampling and shuffling during training, parameter initialization, stochastic regularizers like dropout, and stochastic decoding at inference (temperature, top-k/top-p/nucleus sampling). Expect to both define these sources and discuss their practical effects on model behavior — variance in metrics, mode collapse vs. diversity, and optimization stability.

Typical interview flow

• Brief definition: identify and categorize randomness sources in training vs. inference.
• Diagnostic/design: propose experiments to measure how much each source contributes to output variance (controlled seeds, ablation runs, fixed batches, checkpointing).
• Trade-offs & mitigation: explain reproducibility strategies (seed management, deterministic ops, checkpoint averaging), and production trade-offs (higher temperature → more diverse but less precise).
• Extension: compare sampling strategies, discuss impacts on downstream metrics, or describe distributed-training nondeterminism.

Skill signals interviewers look for

• Solid grasp of probabilistic/stochastic concepts and optimization dynamics
• Practical ML engineering: reproducibility, experiment design, and debugging nondeterminism
• Familiarity with decoding algorithms (temperature, top-k/top-p) and evaluation of generative outputs
• Ability to reason about trade-offs between diversity and fidelity and propose mitigations you can implement in code or CI

Prepare concise examples (one experimental protocol and one production mitigation) to show you can both measure and control randomness in real LLM workflows.