Beyond ChatGMP: Improving LLM generation through user preferences

Prompt engineering � improving the command given to a large language model (LLM) � is becoming increasingly useful in order to maximize the performance of the model and therefore the quality of the output. However, in certain instances, the user is not able to enrich the prompt with additional and personalized details, such as the preferred tone and length of generated response. Therefore, it is useful to create models that learn these preferences and implement them directly in the prompt. Current state-of-the-art inductive logic programming (ILP) systems can play an important role in the development and advancement of digitalization strategies. For example, they can be used to learn personal preferences of users without sacrificing human interpretability of the learned outcomes. These systems have recently witnessed the development of data efficient, robust, and human interpretable algorithms and systems for learning predictive models from data and background knowledge. In this paper, one of these systems, ILASP (inductive learning of answer set programs), is used to develop a proof of concept of how personal preferences of groups of students participating in an interview exercise can be learned to tailor and improve the generated response of a LLM used in an educational context.