Large Language Models (LLMs) excel at generating fluent text but struggle with context sensitivity, logical reasoning, and personalization without extensive fine-tuning. This paper presents a logical modulator: an adaptable communication layer between Knowledge Graphs (KGs) and LLMs as a way to address these limitations. Unlike direct KG-LLM integrations, our modulator is domain-agnostic and incorporates logical dependencies and commonsense reasoning in order to achieve contextual personalization. By enhancing KG interaction, this method will produce linguistically coherent and logically sound outputs, increasing interpretability and reliability in generative AI.

Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual acquisition of these argument structures is highly time-consuming, the corresponding systems are inflexible regarding the topics they can discuss. To address this issue, we propose a combination of argumentative dialogue systems with argument search technology that enables a system to discuss any topic on which the search engine is able to find suitable arguments. Our approach utilizes supervised learning-based relation classification to map the retrieved arguments into a general tree structure for use in dialogue systems. We evaluate the approach with a state of the art search engine and a recently introduced dialogue model in an extensive user study with respect to the dialogue coherence. The results vary between the investigated topics (and hence depend on the quality of the underlying data) but are in some instances surprisingly close to the results achieved with a manually annotated argument structure.