Building Task-Oriented Dialogue Systems via Instruction Guidance without Annotated Data

Task-oriented dialogue (TOD) systems conventionally rely on supervised fine-tuning over large datasets, an approach that is both resource-intensive and difficult to generalize across domains. We investigate whether large language models (LLMs) can serve as effective TOD agents without any fine-tuning, relying solely on in-context prompting and unstructured conversational logs. To this end, we propose a two-stage framework in which an LLM first induces structured procedural instructions from raw multi-turn dialogues, then leverages these instructions to generate goal-oriented interactions. An iterative refinement loop further improves instruction quality by evaluating intermediate dialogue outputs and propagating feedback to update the instructions. To address limitations inherent in existing evaluation protocols, we introduce an interactive evaluation framework centered on a constrained user simulator with access to ground-truth task goals. This design enables flexible assessment of task success beyond fixed dialogue trajectories, more faithfully reflecting the conditions of real-world deployment. Experiments demonstrate that the proposed approach produces coherent and task-effective dialogues without any annotated data. Using Gemma-3-27b-it as the backbone, our system achieves a dialogue state F1 of 86.3%, outperforming GALAXY (84.3%) and MARS (84.6%).