## 4 Conclusions, Limitations, and Future Directions  
This work outlines the development of an adaptive conversation design system aimed at improving the capabilities of social robots in managing multi-party interactions (i.e., group-robot interactions) and addressing their linguistic nuances, particularly the distinction between singular and plural forms of address, which is necessary in many languages, such as German. A significant capability of our system is its ability to discern between individual and group interactions through the utilization of a robust person detection system, based on the YOLOv5m model. This feature enables the social robot to adapt its conversational strategy in accordance with the number of users, which is a crucial capability in public spaces where robots frequently interact with multiple individuals simultaneously. The social robot's approach to handle the social dynamics of group settings represents a step forward compared to systems that focus on one-to-one interactions. The knowledge expander tool, developed as part of this work, leverages LLMs, like GPT-3.5, to automate the generation of diverse training examples for NLU models. This approach significantly reduces the manual effort involved in the creation of the dataset while ensuring that the social robot can handle a wide range of user intents in different languages and interaction contexts. By dynamically expanding responses, also in both singular and plural forms, the knowledge expander plays a crucial role in enhancing the social robot's ability to handle interactions with both individuals and groups. This expansion ensures that the social robot can generate contextually appropriate responses tailored to the specific social dynamics of the interaction. The modular architecture of the system, comprising a robot front end, an intent-based back end, and a LLM back end, enables independent optimization of each component while ensuring that they work effectively. The front end manages key interaction functions, the intent-based back end handles predefined responses and NLU tasks, and the LLM back end generates real-time responses for out-of-knowledge queries. The interaction among these components is critical to the social robot's overall performance. For example, person detection provides real-time data to both the NLU and LLM back ends, allowing the social robot to adjust its response strategy based on the detected number of users. The intent-based back end addresses predefined knowledge with high accuracy, while the LLM back end supplements this by providing contextually appropriate responses for out-of-knowledge queries, thereby maintaining responsiveness in dynamic interaction scenarios. It is particularly important to ensure that prompts fed to the LLM back end are carefully aligned with the knowledge of the intent-based back end. Since the LLM serves as a fallback, it should avoid answering queries already covered by the intent-based system to minimize the risk of generating hallucinated responses. However, when testing the system in the wild, we still discovered the limitation of LLM hallucinations: if the answers were not within the scope of the knowledge database and could not be clearly assigned by the LLM, the LLM would sometimes give supposedly inexplicable answers. This was mostly due to incomplete STT phrases, when the system did not capture the whole user intent, likely to happen when there was a lot of ambient noise in the city hall. Although we tried to counteract this factor with fallbacks, where the system admits not to know the answer, in some cases there were still hallucinations generated by the LLM, a  
Figure 9: Final LLM prompt. topic that needs to be explored more deeply at a broad interdisciplinary level of science. A constraint of our research so far is our focus on English and specifically German as a case study.