Explain LLM lifecycle and trade-offs
GoogleMachine Learning Engineer · Technical Screen
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This question evaluates a candidate's understanding of the end-to-end lifecycle of large language models, covering training data collection and filtering, pretraining objectives, transformer architecture, post-training methods like supervised fine-tuning and preference optimization, and common model or training variants.
Explain the end-to-end lifecycle of a modern large language model. Cover training data collection and filtering, pretraining objectives, transformer architecture, post-training methods such as supervised fine-tuning and preference optimization, and common model or training variants. Discuss the main trade-offs involved, such as quality, safety, latency, cost, and scalability.
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