More than Chit-Chat: Developing Robots for Small-Talk Interactions
This addresses the need for more sociable conversational AI and robots to improve user comfort in social interactions, though it appears incremental as it builds on existing LLM capabilities.
The study tackled the problem of enabling social robots to engage in natural small talk by evaluating Large Language Models (LLMs) and introducing a novel feedback method to align responses with small talk conventions, demonstrating effectiveness in making interactions more realistic and human-like.
Beyond mere formality, small talk plays a pivotal role in social dynamics, serving as a verbal handshake for building rapport and understanding. For conversational AI and social robots, the ability to engage in small talk enhances their perceived sociability, leading to more comfortable and natural user interactions. In this study, we evaluate the capacity of current Large Language Models (LLMs) to drive the small talk of a social robot and identify key areas for improvement. We introduce a novel method that autonomously generates feedback and ensures LLM-generated responses align with small talk conventions. Through several evaluations -- involving chatbot interactions and human-robot interactions -- we demonstrate the system's effectiveness in guiding LLM-generated responses toward realistic, human-like, and natural small-talk exchanges.