  High-quality instruction data is critical for aligning large language models
(LLMs). Although some models, such as Llama-3-Instruct, have open weights,
their alignment data remain private, which hinders the democratization of AI.
High human labor costs and a limited, predefined scope for prompting prevent
existing open-source data creation methods from scaling effectively,
potentially limiting the diversity and quality of public alignment datasets. Is
it possible to synthesize high-quality instruction data at scale by extracting
it directly from an aligned LLM? We present a self-synthesis method for
generating large-scale alignment data named Magpie. Our key observation is that
aligned LLMs like Llama-3-Instruct can generate a user query when we input only
the left-side templates up to the position reserved for user messages, thanks
to their auto-regressive nature. We use this method to prompt Llama-3-Instruct
and generate 4 million instructions along with their corresponding responses.
We perform a comprehensive analysis of the extracted data and select 300K
high-quality instances. To compare Magpie data with other public instruction
datasets, we fine-tune Llama-3-8B-Base with each dataset and evaluate the
performance of the fine-tuned models. Our results indicate that in some tasks,
models fine-tuned with Magpie perform comparably to the official
Llama-3-8B-Instruct, despite the latter being enhanced with 10 million data
points through supervised fine-tuning (SFT) and subsequent feedback learning.
We also show that using Magpie solely for SFT can surpass the performance of
previous public datasets utilized for both SFT and preference optimization,
such as direct preference optimization with UltraFeedback. This advantage is
evident on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.
