Self-QA: Unsupervised Knowledge Guided Language Model Alignment
This addresses the problem of data scarcity and quality for researchers and practitioners in AI, though it appears incremental as it builds on existing instruction tuning methods.
The paper tackles the challenge of creating supervised paired question-answering data for instruction tuning by introducing Self-QA, a framework that uses unsupervised knowledge to generate domain-specific instruction data, resulting in improved data generation without human annotation.
Large-scale language models like ChatGPT and GPT-4 have gained attention for their impressive conversational and generative capabilities. However, the creation of supervised paired question-answering data for instruction tuning presents formidable challenges. This endeavor necessitates substantial human effort for data annotation and wrestles with issues concerning data quality, diversity, accuracy, and other related factors. To overcome these obstacles, we introduce an innovative framework named Self-QA, which replaces the traditional practice of human-written instruction seeds with a vast amount of unsupervised knowledge, enabling the model to generate a larger quantity of correct and domain-specific instruction data. The effectiveness of our proposed method is demonstrated through experiments conducted on unsupervised corpora from various domains.