FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only
Abstract: 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 an expensive and laborious process, often reliant 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, 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.
Paper Type: Long
Research Area: Language Modeling
Research Area Keywords: Instruction-Tuning ; Free Generation
Contribution Types: NLP engineering experiment, Reproduction study, Approaches to low-resource settings, Data resources
Languages Studied: English
Submission Number: 281
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