Synthetic Context Generation for Question Generation

arXiv:2406.13188v1 Announce Type: new
Abstract: Despite rapid advancements in large language models (LLMs), QG remains a challenging problem due to its complicated process, open-ended nature, and the diverse settings in which question generation occurs. A common approach to address these challenges involves fine-tuning smaller, custom models using datasets containing background context, question, and answer. However, obtaining suitable domain-specific datasets with appropriate context is often more difficult than acquiring question-answer pairs. In this paper, we investigate training QG models using synthetic …

abstract arxiv challenges context cs.cl cs.lg custom models datasets diverse fine-tuning language language models large language large language models llms nature problem process question synthetic tuning type