Greedy decoding always takes the largest logit, beam keeps several high-probability sequences, and sampling draws from a temperature-scaled distribution. Top-k fixes candidate count while top-p keeps the smallest probability mass set; quality, diversity, and latency trade off.
Use equations or tensor shapes where they clarify the claim, then name an experiment or ablation that would distinguish competing explanations.
Get asked follow-ups live, then receive a scored report — like a real MLE interview loop.
Start AI mock interview