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.

通过自我综合的方法，从对齐的大型语言模型中直接提取高质量的指令数据，称为 Magpie，以生成大规模的对齐数据

Magpie: 无助推主题、从头训练，通过提示对齐的 LLMs 生成对齐数据

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs  with Nothing

Part of speech tagging in zero-resource settings can be an effective approach
for low-resource languages when no labeled training data is available. Existing
systems use two main techniques for POS tagging i.e. pretrained multilingual
large language models(LLM) or project the source language labels into the zero
resource target language and train a sequence labeling model on it. We explore
the latter approach using the off-the-shelf alignment module and train a hidden
Markov model(HMM) to predict the POS tags. We evaluate transfer learning setup
with English as a source language and French, German, and Spanish as target
languages for part-of-speech tagging. Our conclusion is that projected
alignment data in zero-resource language can be beneficial to predict POS tags.

零资源环境中的词性标注可以是一种有效的方法，当没有标记的训练数据可用时，特别适用于资源匮乏的语言。本文研究了使用预训练的多语言大型语言模型或在零资源目标语言中将源语言标签映射并训练序列标注模型两种主要技术进行词性标注。我们通过现成的对齐模块探索了后一种方法，并训练了一个隐马尔可夫模型来预测词性标签。我们在以英语为源语言，法语、德语和西班牙语为目标语言的词性标注中评估了转移学习设置。我们的结论是，零资源语言中的映射对于预测词性标签是有益的。