Natural Language Processing

This paper presents a detailed study on the fine-tuning of Google’s t5 model aimed at enhancing its ability to generate high-quality, relevant questions. Leveraging the diverse and widely accepted SQuAD dataset, we trained our model specifically for question generation. Our results indicate significant improvements in multiple performance metrics, including BLEU, ROUGE and Word Mover’s Distance scores. The results were evaluated in a qualitative manner using an interactive interface. We discuss the implications of these findings for the field of model based question generation, highlighting potential applications in education. Despite the advancements, we acknowledge certain limitations in terms of generalization. Future work is suggested to further refine the model, explore alternative architectures, and integrate the enhanced model into broader AI systems.

To streamline the generation of high-quality multiple-choice questions (MCQs), two sequential pipelines were created: one with a modular design for question/answer and distractor generation, and another with a single unified model. BART was fine-tuned on the SciQ dataset, and the unified model outperformed the modular model on qualitative measures and cosine similarity of correct answers and distractors. Another pipeline was developed to extend the use of the model: given a URL and a topic, it generates an MCQ. A fine-tuned BART-large model successfully generated MCQs beyond the science domain of the SciQ dataset.