  This paper introduces LLM-MARS, first technology that utilizes a Large
Language Model based Artificial Intelligence for Multi-Agent Robot Systems.
LLM-MARS enables dynamic dialogues between humans and robots, allowing the
latter to generate behavior based on operator commands and provide informative
answers to questions about their actions. LLM-MARS is built on a
transformer-based Large Language Model, fine-tuned from the Falcon 7B model. We
employ a multimodal approach using LoRa adapters for different tasks. The first
LoRa adapter was developed by fine-tuning the base model on examples of
Behavior Trees and their corresponding commands. The second LoRa adapter was
developed by fine-tuning on question-answering examples. Practical trials on a
multi-agent system of two robots within the Eurobot 2023 game rules demonstrate
promising results. The robots achieve an average task execution accuracy of
79.28% in compound commands. With commands containing up to two tasks accuracy
exceeded 90%. Evaluation confirms the system's answers on operators questions
exhibit high accuracy, relevance, and informativeness. LLM-MARS and similar
multi-agent robotic systems hold significant potential to revolutionize
logistics, enabling autonomous exploration missions and advancing Industry 5.0.
