## 3 Development and Results of the Multi-Party Dialogue Management
### Development of the Knowledge Expander  
Unlike a pre-trained LLM, the NLU model within the intent-based back end requires an initial training phase to function effectively. This variability is crucial because it relies on word embeddings to determine intents, which helps achieve high confidence in intent recognition. We developed a LLM-based synthesis tool, the knowledge expander, to accurately map the various needs of different users in public spaces. This ensures sufficient variation in NLU training while reducing the required manual effort. Generally, at least 20 training examples per user intent and language are needed to train the NLU model effectively. Ideally, this would mean that, with knowledge expander, only one human-written example sentence per user intent would be required to generate at least 20 training examples for it [41]. In a multilingual setting, a single English example sentence could also be used to generate 20 training examples in different languages. However, training our NLU model with generated examples based on one human-created example per intent was not successful. This is attributed to the fact that, in training a NLU model, not only is the number of training examples critical, but also the quality, the variance between the examples, and the conceptual distance between different user intents. In our case, the training examples generated with the knowledge expander were not sufficiently diverse, leading to numerous errors during NLU training that could not be manually corrected. By analyzing these errors, we observed that the examples generated by the LLM based on a single human-written example sometimes differed by only one word, such as \"Can I use the toilet?\" versus \"Can I use the toiletts?\". This issue was addressed by providing three manually created examples per intent in knowledge expander, which varied more significantly in phrasing. In this manner, the LLM was able to more accurately comprehend the intent's meaning, consequently producing a greater degree of diversity in the training examples. Taking into account the hardware conditions, the GPT-3.5 [18] API was used, given the substantial quantitative need for the generated training data. The knowledge database utilized in the context of the city administration included 222 different intents. After expansion, we received 19,621 (\(M\) = 88.38) training examples per language in German, English, French, Italian, Spanish, Polish, Russian, and Turkish. This expansion resulted in a total of 156,864 training examples across all eight languages. Given the GPU memory constraints, the locally running model LLaMA 2 could not be parallelized, which would result in the generation of these examples lasting several days. Although the generation speed of a single training example via the GPT-3.5 API does not significantly exceed that of LLaMA 2, using GPT-3.5 allows for parallelization. Since user intents could be expanded independently, we utilized an arbitrary number of threads to generate training examples. As a consequence of the limitations of the API in terms of the number of requests that can be made per unit of time, this generation method still required several hours to complete. When creating the 156,864 training examples in eight languages, some conflicts still required manual resolution during the NLU model training. The aforementioned conflicts were attributable to the existence of intents that were inherently analogous, such as \"Reisepass_abholen\" (pick up passport) versus \"Reisepass_beantragen\" (apply for passport). This resulted in the generation of analogous training examples that could be applied to both intents, thus precipitating errors during NLU training. It was also observed that the training instances generated sometimes lacked sufficient variability or were even identical.