Protege: Prompt-based diverse question generation from web articles

Rich and diverse knowledge-bases (KB) are foundational building blocks for online knowledge-sharing communities such as StackOverflow and Quora and applications such as conversational assistants (aka chatbots). A popular format for knowledge bases is question-answer pairs (or FAQs), where questions are designed to accurately match a multitude of queries. In this paper, we address the problem of automatic creation of such Q&A-based knowledge bases from domain-specific, long-form textual content (e.g., web articles). Specifically, we consider the problem of question generation, which is the task of generating questions given a paragraph of text as input, with a goal to achieve both diversity and fidelity of the generated questions. Towards this goal we propose Protege, a diverse question generation framework which consists of (1) a novel encoder-decoder-based Large Language Model (LLM) architecture which can take a variety of prompts and generate a diverse set of candidate questions, and (2) a hill-climbing algorithm that maximizes a sub-modular objective function to balance diversity with fidelity. Through our experiments on three popular public Q&A datasets, we demonstrate that Protege improves diversity by +16% and fidelity by +8% over diverse beam search and prompt-based baselines.