  This paper presents the design and evaluation of a novel multi-level LLM
interface for supermarket robots to assist customers. The proposed interface
allows customers to convey their needs through both generic and specific
queries. While state-of-the-art systems like OpenAI's GPTs are highly adaptable
and easy to build and deploy, they still face challenges such as increased
response times and limitations in strategic control of the underlying model for
tailored use-case and cost optimization. Driven by the goal of developing
faster and more efficient conversational agents, this paper advocates for using
multiple smaller, specialized LLMs fine-tuned to handle different user queries
based on their specificity and user intent. We compare this approach to a
specialized GPT model powered by GPT-4 Turbo, using the Artificial Social Agent
Questionnaire (ASAQ) and qualitative participant feedback in a counterbalanced
within-subjects experiment. Our findings show that our multi-LLM chatbot
architecture outperformed the benchmarked GPT model across all 13 measured
criteria, with statistically significant improvements in four key areas:
performance, user satisfaction, user-agent partnership, and self-image
enhancement. The paper also presents a method for supermarket robot navigation
by mapping the final chatbot response to correct shelf numbers, enabling the
robot to sequentially navigate towards the respective products, after which
lower-level robot perception, control, and planning can be used for automated
object retrieval. We hope this work encourages more efforts into using
multiple, specialized smaller models instead of relying on a single powerful,
but more expensive and slower model.
