FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only
<|reference_start|>FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only: Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets has traditionally been expensive and laborious, often relying on manual annotations or costly API calls of proprietary LLMs. To address these challenges, we introduce FANNO, a fully autonomous, open-sourced framework that revolutionizes the annotation process without the need for pre-existing annotated data. Utilizing a Mistral-7b-instruct model, FANNO efficiently produces diverse and high-quality datasets through a structured process involving document pre-screening, instruction generation, and response generation. Experiments on Open LLM Leaderboard and AlpacaEval benchmark show that the FANNO can generate high-quality data with diversity and complexity for free, comparable to human-annotated or cleaned datasets like Alpaca-GPT4-Cleaned.<|reference_end|>
@article{zhu2024fanno:,
    title={FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs
  Only},
    author={He Zhu, Junyou Su, Tianle Lun, Yicheng Tao, Wenjia Zhang, Zipei Fan,
  Guanhua Chen},
    journal={arXiv preprint arXiv:2408.01323},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01323},
    primaryClass={cs.CL}
}