QOG:Question and Options Generation based on Language Model
This work addresses automated multiple-choice question generation for applications like education and model fine-tuning, but it is incremental as it builds on existing language model methods.
The paper tackled the problem of generating question-options pairs from context, achieving competitive performance with a large language model while being computationally efficient and stable.
Question-Options Generation (QOG) is a task that involves generating a set of question-options pairs given context. This task has various applications, including fine-tuning large models, information retrieval, and automated multiple-choice question generation for education. In this paper, we develop QOG models using three different methods based on fine-tuning sequence-to-sequence language models (LMs). Experiments demonstrate that the end-to-end QOG model is computationally efficient and stable during both training and inference, outperforming other methods. Furthermore, our analysis indicates that our QOG models are competitive on the QOG task compared to the large language model Llama 3-8B.