In Clippers on Tuesday, I’m going to present on the beginning stages of a new project. I’m attempting to design a response generation model for the COSI museum avatar — a virtual question-answering guide at the Language Pod that can answer questions about the pod, linguistics, and other exhibits at COSI. Currently, the avatar, which is modeled after the Virtual Patient project, returns “canned” responses to questions, meaning that it has prescribed, static answers for a set of in-domain questions to which it tries to match user inputs. This can result in a fairly unnatural conversation; if the avatar interprets two utterances as the same question, it will repeat the exact same answer. The goal of my current project is to migrate to using a response generation model that will be more contextually aware and answer questions dynamically, but also adapt to constant changes in content as exhibits in the museum change. To do so, I’m attempting to leverage the capabilities of OpenAI’s ChatGPT to generate training data for a smaller model that will hopefully avoid the pitfalls of LLMs such as toxic behavior. The plan is to eventually train a document-grounded generation model that responds directly to user inputs rather than needing to first map them to prescribed questions. This project is in the early exploratory phases, so I’m hoping to get lots of feedback on design choices and suggestions for other avenues to explore.