Can Large Language Models Be Good Companions? An LLM-Based Eyewear System with Conversational Common Ground
CoRR(2023)
摘要
Developing chatbots as personal companions has long been a goal of artificial
intelligence researchers. Recent advances in Large Language Models (LLMs) have
delivered a practical solution for endowing chatbots with anthropomorphic
language capabilities. However, it takes more than LLMs to enable chatbots that
can act as companions. Humans use their understanding of individual
personalities to drive conversations. Chatbots also require this capability to
enable human-like companionship. They should act based on personalized,
real-time, and time-evolving knowledge of their owner. We define such essential
knowledge as the \textit{common ground} between chatbots and their owners, and
we propose to build a common-ground-aware dialogue system from an LLM-based
module, named \textit{OS-1}, to enable chatbot companionship. Hosted by
eyewear, OS-1 can sense the visual and audio signals the user receives and
extract real-time contextual semantics. Those semantics are categorized and
recorded to formulate historical contexts from which the user's profile is
distilled and evolves over time, i.e., OS-1 gradually learns about its user.
OS-1 combines knowledge from real-time semantics, historical contexts, and
user-specific profiles to produce a common-ground-aware prompt input into the
LLM module. The LLM's output is converted to audio, spoken to the wearer when
appropriate.We conduct laboratory and in-field studies to assess OS-1's ability
to build common ground between the chatbot and its user. The technical
feasibility and capabilities of the system are also evaluated. OS-1, with its
common-ground awareness, can significantly improve user satisfaction and
potentially lead to downstream tasks such as personal emotional support and
assistance.
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