Taking LLMs out of the Black Box: A Practical Guide to Human-in-the-Loop Distillation
SMRTR summary
Large language models can be distilled into smaller, task-specific models that are faster, more transparent, and data-private. This approach uses human-in-the-loop annotation to create targeted datasets, enabling models that often outperform few-shot LLM baselines while being 20+ times faster and only megabytes in size.
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