Task-Oriented Dialogue (TOD) systems are designed to carry out specific tasks by tracking dialogue states and generating appropriate responses to help users achieve defined goals. Recently, end-to-end dialogue models pre-trained based on large datasets have shown promising performance in the conversational system. However, they share the same parameters to train tasks of the dialogue system (NLU, DST, NLG), so debugging each task is challenging. Also, they require a lot of effort to fine-tune large parameters to create a task-oriented chatbot, making it difficult for non-experts to handle. Therefore, we intend to train relatively lightweight and fast models compared to PLM. In this paper, we propose an End-to-end TOD system with Task-Optimized Adapters which learn independently per task, adding only small number of parameters after fixed layers of pre-trained network. We also enhance the performance of the DST and NLG modules through reinforcement learning, overcoming the learning curve that has lacked at the adapter learning and enabling the natural and consistent response generation that is appropriate for the goal. Our method is a model-agnostic approach and does not require prompt-tuning as only input data without a prompt. As results of the experiment, our method shows competitive performance on the MultiWOZ benchmark compared to the existing end-to-end models. In particular, we attain state-of-the-art performance on the DST task of 2.2 dataset.

As Task-Oriented Dialog (TOD) systems have advanced, structured DB systems, which aim to collect relevant knowledge for answering user’s questions, have also progressed. Despite these advancements, these methods face challenges when dealing with subjective questions from users. To overcome this, DSTC11 released a subjective-knowledge-based TOD (SK-TOD) dataset and benchmark. This paper introduces a framework that effectively solves SK-TOD tasks by leveraging a Large Language Model (LLM). We demonstrate the proficient use of LLM for each sub-task, including an adapters-based method and knowledge-grounded data augmentation. Our proposed methods, which utilize LLM as an efficient tool, outperform baseline performance and approaches that directly use LLM as a one-step sub-task solver, showing superior task-specific optimization.