Chatbot Dialog Design for Improved Human Performance in Domain Knowledge Discovery

Abstract

The advent of machine learning (ML) has led to the widespread adoption of developing task-oriented dialog systems for scientific applications (e.g., science gateways) where voluminous information sources are retrieved and curated for domain users. Yet, there still exists a challenge in designing chatbot dialog systems that achieve widespread diffusion among scientific communities. In this article, we propose a novel Vidura advisor design framework (VADF) to develop dialog system designs for information retrieval (IR) and question-answering (QA) tasks, while enabling the quantification of system utility based on human performance in diverse application environments. We adopt a socio-technical approach in our framework for designing dialog systems by utilizing domain expert feedback, which features a sparse retriever for enabling accurate responses in QA settings using linear interpolation smoothing. We apply our VADF for an exemplar science gateway, viz. KnowCOVID-19, to conduct experiments that demonstrate the utility of dialog systems based on IR and QA performance, application utility, and perceived adoption. Experimental results show our VADF approach significantly improves IR performance against retriever baselines (up to 5% increase) and QA performance against large language models (LLMs) such as ChatGPT (up to 43% increase) on scientific literature datasets. In addition, through a usability survey, we observe that measuring application utility and human performance when applying VADF to KnowCOVID-19 translates to an increase in perceived community adoption.