ACAT-G: An Interactive Learning Framework for Assisted Response Generation

Abstract

In this paper, we introduce ACAT-G, an interactive dialogue learning framework that incorporates constant human feedback into fine-tuning language models in order to assist conditioned dialog generation. The system takes in a limited amount of input from a human and generates personalized response corresponding to the context of the conversation within natural dialog time-frame. By combining inspirations from online learning, reinforcement learning, and large scale language models, we expect this project to provide a foundation for human-in-the-loop conditional dialog generation tasks.

core technology behind and beyond ChatGPT

“…developed a model to detect confusion expressed among students' comments in course forums Dyulicheva (2021). studied the emotional states of learners associated with math anxiety on MOOCs Lu et al (2021). provided support in generating personalized responses that correspond to the context of a conversation.…”