FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only

TL;DR

FANNO is an open-source framework that automates high-quality instruction data generation for LLMs, reducing costs and effort while achieving results comparable to human-annotated datasets.

Contribution

FANNO introduces a fully autonomous, open-source method for generating diverse instruction datasets using open LLMs, eliminating the need for manual annotation or proprietary API calls.

Findings

FANNO produces high-quality, diverse datasets comparable to human annotations.

FANNO reduces annotation costs and effort significantly.

Experiments show FANNO's data improves LLM performance on benchmarks.

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 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.