Spec- TOD : A Specialized Instruction-Tuned LLM Framework for Efficient Task-Oriented Dialogue Systems (2025)

Abstract: Task-oriented dialogue (TOD) systems facilitate goal-driven interactions between users and machines. While recent advances in deep learning have improved the performance, TOD systems often struggle in low-resource scenarios with limited labeled data. To address this challenge, we propose Spec-TOD, a novel framework designed to train an end-to-end TOD system with limited data. Spec-TOD introduces two main innovations: (i) a novel specialized end-to-end TOD framework that incorporates explicit task instructions for instruction-tuned large language models (LLMs), and (ii) an efficient training strategy that leverages lightweight, specialized LLMs to achieve strong performance with minimal supervision. Experiments on the MultiWOZ dataset, a widely used TOD benchmark, demonstrate that Spec-TOD achieves competitive results while significantly reducing the need for labeled data. These findings highlight the potential of the proposed framework in advancing efficient and effective TOD systems in low-resource settings.

## introduction task-oriented dialogue (tod) systems have emerged as a critical area of research in natural language processing (nlp), integrating multiple functionalities, such as understanding user input, dialogue state tracking, and response generation to effectively support users in accomplishing specific objectives @xcite . nevertheless, the development of these systems continues to pose significant challenges @xcite . one of the primary obstacles in creating an effective tod system is the requirement for extensive annotation and frequent retraining @xcite . specifically, prevailing tod models either finetune pre-trained language models such as gptmethods struggle to generalize across new domains and remain suboptimal. recent advancements in large language models (llms), including proprietary models (e.g., gpt series @xcite ) and open-source models (e.g., llama series @xcite ), have transformed nlp tasks by enabling zero-shot and few-shot generalization across various tasks, including the development of llm-powered tod systems (figure 1 (b) ). furthermore, emerging agent-based llm technologies have reshaped multi-task learning in tod systems by leveraging general-purpose, instructionfollowing models (e.g., gpt-4) that autonomously execute tasks through predefined external apis @xcite . nonetheless, current llm-based approaches, including agentic llms, encounter two key limitations in end-to-end tod systems: (i) the efficacy of zero-shot prompting, whether guided by schema or driven by api-based task specifications in agent-based architectures, is heavily contingent on the underlying llm's capabilities. competitive performance typically necessitates extremely large models, often with hundreds of billions of parameters. such models entail significant computational costs, pose challenges for local deployment, and limit accessibility; (ii) relying solely on instructional prompts to execute all tod tasks, such as dialogue state tracking (dst) and natural language generation (nlg), may lack adaptability. this issue often results in suboptimal performance, especially in complex dialogue scenarios @xcite . building on this analysis of current challenges, we formulate the following research question: how can the performance of end-to-end tod systems be improved by leveraging trainable llms with limited labeled data? to address this question, this study proposes spec-tod, a novel framework designed to optimize task-oriented dialogue systems by utilizing trainable llms for unifying multi-specified tasks via instruction tuning under constraints of limited labeled datasets. the main idea is to specify individual tasks with tailored instructions, enabling unified multi-task learning via instruction-tuned llms. accordingly, spec-tod leverages multiple tasks of end-to-end tod systems with specified instruction representations and a parameter-efficient tuning technique, enabling it to achieve competitive performance while utilizing much smaller models and limited computational resources. the primary contributions of this study are threefold: ## methodology this section establishes the foundation for our work by first providing background information on endto-end tod systems. subsequently, we delve into the details of our proposed framework. ## conclusion tod systems have long posed challenges in nlp due to limited flexibility and scalability to unseen domains. recent emerging llm technologies have revolutionized nlp applications by enabling zeroshot and few-shot generalization capabilities. however, utilizing llms for tasks requiring knowledge grounding, such as tod, continues to be a critical challenge that warrants further study, especially for end-to-end tod systems. therefore, this study presents a novel few-shot learning method for endto-end tod systems via instruction tuning with open-weight llms. the evaluation of various versions of the multiwoz datasets indicates promising results of our proposed method with 10% of the training samples.