Prompt-based Language Generation for Complex Conversational Coaching Tasks across Languages (2025)

Abstract: We investigate the role of prompt-based demonstrators in improving natural language generation for coaching-oriented dialogue systems in different languages. These systems present significant challenges due to their need for semantically accurate, goal-driven responses across diverse dialogue act taxonomies and languages. We define three types of prompt demonstrators, i.e., pairs of meaning representation-utterance, that include different degrees of specification in such meaning representation. We then fine-tune pretrained language models separately for four very different languages and evaluate how the specificity of these demonstrators affects the quality of the generated sentences. Our experiments show that more specific prompts lead to more coherent and accurate outputs, particularly for low-resource languages and small models. Additionally, we observe promising zero-shot performance with larger models, showing a complementary value of prompts. These results demonstrate that simple prompting strategies, combined with fine-tuning, can significantly improve output quality in complex dialogue generation tasks across languages.

## introduction dialogue systems for coaching and healthcare applications must handle complex, user-centered interactions that go beyond giving or requesting information. these systems aim to support behavioral change through goal-oriented conversations, a challenge that requires high control and contextual awareness @xcite . traditional rule-based systems fall short in flexibility, while large language models (llm)-based agents offer better adaptability but raise concerns about control and factual consistency, especially in sensitive domains like health @xcite . recent work emphasizes the importance of pretrained language models and prompt-based learning for improving generation in such scenarios @xcite . these methods allow models to generalize from a few examples using incontext learning, which is especially valuable when annotated data is limited or multilingual coverage is required @xcite . for goal-setting dialogues, even small variations in the input meaning representation (mr) can significantly affect the response's quality and intent realization @xcite . despite the wide research in prompt-based learning, the effects of some structural variations in the demonstrators are underexplored. in this work, we expand our research on how the similarity between mr inputs and demonstrators affects natural language generation in different domains @xcite ) by introducing a cross-lingual perspective in a specific use case: complex dialogue systems in coaching contexts. ## empathic task the empathic project @xcite @xcite aimed to build a virtual coach that can assist users in changing their unhealthy habits, if any, through conversations. this virtual coach does not generally give advice or information. on the contrary, it follows the grow model @xcite , a behavioral model that promotes users' reflection with questions. based on this model, the virtual coach first encourages the users to look for a goal, making them aware of the possible weakness of their routines, in particular nutritional routines, by assessing their reality. next, the users explore their options and will to act, defining a plan for goals such as reducing the sugar/salt intake or a regular schedule for meals. consequently, the empathic task implies higher complexity for a conversational system than traditional information providers @xcite . in this context, woz experiments @xcite were carried out in spanish, french, or norwegian, the target languages of the project. in these experiments, a real user and a wizard, a person playing the role of the virtual coach, simulated a coaching session. these coaching sessions were transcribed and translated into english as well as into the three target languages. therefore, the resulting empathic corpus foot_0 is a dataset with equivalent versions in four languages. note that one of them is a low-resourced one: norwegian. ## meaning representations prior works @xcite @xcite have shown that mrs, including an example of the task to perform in the input, i.e., a task demonstrator, generate more accurate and natural sentences. in this work, we aim to confirm the effectiveness of these kinds of representations for complex tasks, already demonstrated in english @xcite , across languages. specifically, we consider three mrs with a demonstrator in the input, the prompt representations. then, we compare them with the original mr of the empathic corpus, the baseline representation. ## experiments the cross-lingual experiments that we present in this section follow the methodologies proposed in @xcite by using the same setup and evaluation metrics but adding new language models for non-english languages. ## conclusions this work explored how prompt-based inputs can enhance natural language generation in cross4 we denote these sequences @xmath1. our results show that increasing the similarity between the demonstrator and the input meaning representation improves both coherence and accuracy, especially when using smaller pre-trained models. these findings suggest that prompting strategies can meaningfully improve generation quality in highly structured, goal-driven dialogues. future directions include testing prompt transferability across languages and domains, and integrating such strategies into real-time interactive systems.