Conversational Agents and Language Models that Learn from Human Dialogues to Support Design Thinking

Abstract

Design is a highly creative and challenging task and research has already explored possible ways for using conversational agents (CAs) to support humans participating in co-design sessions. However, research reports that a) humans in these sessions expect more essential support from CAs, and b) it is important to develop CAs that continually learn from communication -like humans do- and not simply from labeled datasets. Addressing the above needs, this paper explores the specific question of how to extract useful knowledge from human dialogues observed during co-design sessions and make this knowledge available through a CA supporting humans in similar design activities. In our approach we explore the potential of the GPT-3 Large Language Model (LLM) to provide useful output extracted from unstructured data such as free dialogues. We provide evidence that by implementing an appropriate “extraction task” on the LLM it is possible to efficiently (and without human-in-the-loop) extract knowledge that can then be embedded in the cognitive base of a CA. We identify at least four major steps/assumptions in this process that need to be further researched, namely: A1) Knowledge modeling, A2) Extraction task, A3) LLM-based facilitation, and A4) Humans’ benefit. We provide demonstrations of the extraction and facilitation steps using the GPT-3 model and we also identify and comment on various worth exploring open research questions.