Generating Proactive Suggestions based on the Context: User Evaluation of Large Language Model Outputs for In-Vehicle Voice Assistants

Lesley-Ann Mathis, Can Günes, Kathleen Entz, David Lerch, Frederik Diederichs, Harald Widlroither

Abstract: Large Language Models (LLMs) have recently been explored for a variety of tasks, most prominently for dialogue-based interactions with users. The future in-car voice assistant (VA) is envisioned as a proactive companion making suggestions to the user during the ride. We investigate the use of selected LLMs to generate proactive suggestions for a VA given different context situations by using a basic prompt design. An online study with users was conducted to evaluate the generated suggestions. We demonstrate the feasibility of generating context-based proactive suggestions with different off-the-shelf LLMs. Results of the user survey show that suggestions generated by the LLMs GPT4.0 and Bison received an overall positive evaluation regarding the user experience for response quality and response behavior over different context situations. This work can serve as a starting point to implement proactive interaction for VA with LLMs based on the recognized context situation in the car.