Controlling Dialogue Systems with Graph-Based Structures (2025)

Abstract: Large Language Models (LLMs) have significantly advanced the capabilities of dialogue systems, yet they often lack controllability and consistency. My research investigates how explicit structure can be used to guide LLM-based dialogue systems, focusing in particular on graph-based methods. One line of work explores the use of dialogue flow graphs to represent possible user and system actions, enabling systems to constrain generation to goal-directed paths. These graphs serve as an interpretable interface between high-level dialogue policy and low-level natural language output, improving reliability and transparency. In parallel, I examine Retrieval-Augmented Generation (RAG) approaches that leverage knowledge graphs to ground responses in structured background information. I have evaluated how GraphRAG performs on dialogue data and contributed to methods for retrieving compact, relevant subgraphs to support contextually appropriate and verifiable responses. These approaches address the limitations of unguided retrieval and help integrate external knowledge into the generation process more effectively. Together, these directions aim to improve the controllability, grounding, and robustness of LLM-based dialogue systems. I am particularly interested in how graph-based representations can be used not only to structure knowledge, but also to inform and constrain interaction patterns.

## research interests my research focuses on developing methods to guide response generation in llm-based dialogue systems. one approach involves learning dialogue policies through reinforcement learning, where reward signals from a user simulator influence the system's output behavior. another approach investigates how dialogue flows represented as graph structures can be used to explicitly constrain system output. the system's behavior can be structured along predefined paths of system and user actions, with the goal to improve reliability. beyond dialogue control, i also work on retrieval-augmented generation (rag) @xcite with knowledge graphs to ground responses. i have evaluated how graphrag @xcite performs on dialogue data and contributed to a method for retrieving compact and relevant subgraphs to improve answer quality @xcite . finally, i explore how emotion-aware dialogue systems can be used to adapt their behavior based on the user's emotional state. i examined how using detected user emotion to influence response generation affects user perception in human-robot interaction, and compared different approaches to emotion classification in dialogue. ## spoken dialogue system (sds) research dialogue systems, especially those powered by large language models, are increasingly becoming part of everyday workflows and decision-making processes. their natural language interface lowers the barrier to access, allowing people of all backgrounds to interact with complex systems. however, this naturalness can also be misleading: when llms produce fluent but incorrect outputs, users may struggle to recognize inaccuracies @xcite . this highlights the need for better control mechanisms-such as dialogue policies, retrieval modules, or knowledge graph integration-to guide system behavior. researchers today are well-equipped to address these challenges. with both the technical understanding to improve these systems and the awareness to do so responsibly, we can design dialogue systems that are safe, fair, and useful in practice. one central challenge is to reliably infer during the interaction what users want, especially in open-ended or ambiguous scenarios. as dialogue systems take on the role of interfaces to systems that used to require expert knowledge, they must be robust enough to handle diverse users and use cases. addressing this challenge is essential for building dialogue systems that are inclusive, trustworthy, and effective.