###### Abstract  
In this paper, we detail the technical development of a conversation design that is sensitive to group dynamics and adaptable, taking into account the subtleties of linguistic variations between dyadic (i.e., one human and one agent) and group interactions in human-robot interaction (HRI) using the German language as a case study. The paper details the implementation of robust person and group detection with YOLOv5m and the expansion of knowledge databases using large language models (LLMs) to create adaptive multi-party interactions (MPIs) (i.e., group-robot interactions (GRIs)). We describe the use of LLMs to generate training data for socially interactive agents including social robots, as well as a self-developed synthesis tool, knowledge expander, to accurately map the diverse needs of different users in public spaces. We also outline the integration of a LLM as a fallback for open-ended questions not covered by our knowledge database, ensuring it can effectively respond to both individuals and groups within the MPI framework. human-robot interaction; large language model; social robot; multi-party interaction; group-robot interaction +
Footnote †: journal: Applied sciences  
an intent-based system represents a costly and time-consuming process that is not easily scalable due to the need for human input [14]. In previous instances, crowd workers have been employed to categorize and provide annotations for datasets intended for the training of NLU models [15]. This presents a challenge, as the expected quality level was not consistently met [16], and is often associated with precarious working conditions, insufficient compensation, and a lack of social protection for workers. Along with this, attempts have been made to automate the process of generating NLU training data, using intent-based techniques that involve the construction of sentences by selecting synonym words from a predefined list such as '(hello l hi) (1 mighty) world', resulting in variations like 'hello mighty world', 'hello world', 'hi mighty world', and 'hi world' [17]. However, this approach requires a significant amount of manual effort and could lead to a lack of diversity in the training data [16]. With the release of large language models (LLMs) and the emergence of models such as GPT-4 (OpenAI, San Francisco, CA, USA) [18] and LLaMA 3 (Meta, Menlo Park, CA, USA) [19], LLMs are increasingly being used in the field of human-machine interaction (HMI), among many other aspects, with the aim of tackling challenges related to knowledge representation [20] and to generate training data [21] for chatbots, voice assistants, and social robots. In the latter case, the natural language processing (NLP) and generation capabilities of LLMs are employed to generate training data that are more semantically accurate, which accelerates the process and potentially necessitates less manual correction in comparison to intent-based approaches. This is especially beneficial since large language models (LLMs) are often impractical for real-time applications because of significant computational expenses, as well as quality control and compliance requirements; however, they can also serve in generating training data. One potential avenue for utilizing LLMs in this context is to take advantage of their few shot learning capabilities, as demonstrated by [PERSON] et al. [21]. In this case, the LLM is provided with a single example of a translation or paraphrasing task and is then able to sample a multitude of new and diverse examples from this source. However, it is increasingly apparent that existing LLMs are constrained by a number of challenges, including the generation of hallucinated data, the introduction of prejudices, and the execution of intricate computations. These challenges encompass elements such as a large number of parameters and substantial memory needs, requiring adaptable approaches for robotics that do not depend on hardware setups such as graphics processing unit (GPU) clusters for inference.