Towards LLM -driven Dialogue State Tracking (2023)

Abstract: Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. In this study, we conduct an initial examination of ChatGPT’s capabilities in DST. Our evaluation uncovers the exceptional performance of ChatGPT in this task, offering valuable insights to researchers regarding its capabilities and providing useful directions for designing and enhancing dialogue systems. Despite its impressive performance, ChatGPT has significant limitations including its closed-source nature, request restrictions, raising data privacy concerns, and lacking local deployment capabilities. To address these concerns, we present LDST, an LLM-driven DST framework based on smaller, open-source foundation models. By utilizing a novel domain-slot instruction tuning method, LDST achieves performance on par with ChatGPT. Comprehensive evaluations across three distinct experimental settings, we find that LDST exhibits remarkable performance improvements in both zero-shot and few-shot setting compared to previous SOTA methods. The source code is provided for reproducibility.

## introduction task-oriented dialogue systems have emerged as powerful tools for assisting users in accomplishing a wide range of tasks @xcite . these systems, such as apple siri and microsoft cortana, function as virtual personal assistants, providing support for tasks like flight reservations, appointment scheduling, and hotel bookings. dialogue state tracking (dst) plays a crucial role in taskoriented dialogue systems by accurately tracking figure 1 : example of a multi-domain dialogue. the slots "hotel-pricerange" and "restaurant-pricerange" have a co-reference relationship, where the value of the former is inferred from the latter. the slot "restaurantarea" demonstrates error propagation behavior. the evolving user goals and system actions during a conversation. in general, the multi-domain dialogue state is represented as a list of triplets in the form of (domain, slot, value), e.g., "<restaurant, area, @xmath0. these predefined slot pairs are extracted from the dialogue context at each turn. a plethora of models have been proposed to address the challenges of multi-domain dst, as documented in recent studies @xcite @xcite @xcite @xcite . these models primarily focus on effective transfer and generalization across diverse domains, addressing the crucial challenges of co-reference @xcite and error propagation problem @xcite depicted in figure 1 . the co-reference challenge poses a significant hurdle in enhancing dst performance, as it arises from the linguistic variations in multi-turn dialogues where slots and values are often indirectly expressed. moreover, the error propagation issue emerges when the model fails to recognize and rectify errors in the previously predicted dialogue state, leading to the persistence of errors in subsequent turns. despite significant efforts to address these issues, they persist as ongoing challenges. in recent days, the emergence of large-scale pretrained language models has revolutionized the field of natural language processing (nlp). models like chatgpt foot_0 have shown excellent performance, sparking significant interest in evaluating their effectiveness across different dimensions @xcite @xcite @xcite . despite the significant advancements made by large language models (llms), their performance in multi-domain dst remains relatively unexplored. to bridge this research gap, we conduct an evaluation of chatgpt's capabilities for dst. the evaluation unveils chatgpt's exceptional performance in the dst task, offering valuable insights to researchers and providing useful directions for further exploration. while chatgpt demonstrates superb performance, it has significant limitations @xcite @xcite . firstly, it is not open source, so the underlying code and model parameters cannot be modified by users. second, it is subject to request limitations, which can restrict its usage in high-demand scenarios. furthermore, there are concerns regarding strong data privacy protection, as the system may collect and store user data. lastly, chatgpt cannot be deployed locally, limiting its availability and control. these limitations hinder the applicability and adoption of chatgpt in various practical scenarios for building task-oriented dialogue systems. to overcome the limitations of chatgpt, we introduce ldst, a dst framework driven by llms but based on smaller, open-source foundation models. ldst employs a novel assembled domainslot instruction tuning method and a parameter efficient tuning technique, enabling it to achieve performance comparable to chatgpt while utilizing a much smaller model and limited computational resources. ldst demonstrates exceptional performance across three different experimental settings, surpassing prior state-of-the-art methods by a large margin and demonstrating its remarkable adaptability and generalization capabilities. our main contributions are concluded as follows: ## assessing the capabilities of chatgpt for dst in this section, we evaluate the effectiveness of chatgpt in addressing the dst task. before going into detail, we first formally define the problem. ## fine-tuning smaller foundation models with instructions for dst to overcome the aforementioned limitations of chatgpt, we introduce ldst, an llmdriven dst framework that leverages fine-tuning smaller, open-source foundation models such as llama @xcite with instructions specially tailored for dst. we first outline the process of constructing an instruction dataset for the multi-domain dst task. next, we utilize a parameter-efficient fine-tuning (peft) technique to train the foundation model with the instruction dataset. peft enables the training of a foundation model with limited computational resources. ## conclusion in this study, we conduct an initial examination of chatgpt's capabilities in multi-domain dst, showcasing its superiority over previous methods. this comprehensive evaluation provides useful directions for researchers to design and enhance dialogue systems. to solve the limitations of chatgpt, we present ldst, an llm-driven dst framework based on smaller, open-source foundation models. by utilizing a novel assembled domain-slot instruction tuning method, ldst achieves performance on par with chatgpt. comprehensive evaluations across three distinct experimental settings demonstrate the remarkable performance improvements achieved by ldst compared to previous methods.