1 research outputs found
NewsGPT: ChatGPT Integration for Robot-Reporter
The integration of large language models (LLMs) with social robots has
emerged as a promising avenue for enhancing human-robot interactions at a time
when news reports generated by artificial intelligence (AI) are gaining in
credibility. This integration is expected to intensify and become a more
productive resource for journalism, media, communication, and education. In
this paper a novel system is proposed that integrates AI's generative
pretrained transformer (GPT) model with the Pepper robot, with the aim of
improving the robot's natural language understanding and response generation
capabilities for enhanced social interactions. By leveraging GPT's powerful
language processing capabilities, this system offers a comprehensive pipeline
that incorporates voice input recording, speech-to-text transcription, context
analysis, and text-to-speech synthesis action generation. The Pepper robot is
enabled to comprehend user queries, generate informative responses with general
knowledge, maintain contextually relevant conversations, and act as a more
domain-oriented news reporter. It is also linked with a news resource and
powered with a Google search capability. To evaluate the performance of the
framework, experiments were conducted involving a set of diverse questions. The
robot's responses were assessed on the basis of eight criteria, including
relevance, context, and fluency. Despite some identified limitations, this
system contributes to the field of journalism and human-robot interaction by
showcasing the potential of integrating LLMs with social robots. The proposed
framework opens up opportunities for improving the conversational capabilities
of robots, enabling interactions that are smoother, more engaging, and more
context aware