Training · Data

Instruction Tuning with GPT-4

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First page

The curator’s take

Uses GPT-4 to generate instruction-following data for LLM fine-tuning.

Key points

GPT-4 as data generator: First systematic attempt to use GPT-4 (rather than human annotators) to produce instruction-following data.

52K bilingual examples: Releases 52K unique English and Chinese instruction-following examples.

LLaMA fine-tuning: Uses the dataset to instruction-tune LLaMA models, leading to superior zero-shot performance on new tasks.

Synthetic data wave: Part of the 2023 wave establishing synthetic data from strong models as the dominant alignment data source.

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