D iv TOD : Unleashing the Power of LLM s for Diversifying Task-Oriented Dialogue Representations (2024)

Abstract: Language models pre-trained on general text have achieved impressive results in diverse fields. Yet, the distinct linguistic characteristics of task-oriented dialogues (TOD) compared to general text limit the practical utility of existing language models. Current task-oriented dialogue pre-training methods overlook the one-to-many property of conversations, where multiple responses can be appropriate given the same conversation context.In this paper, we propose a novel dialogue pre-training model called DivTOD, which collaborates with LLMs to learn diverse task-oriented dialogue representations. DivTOD guides LLMs in transferring diverse knowledge to smaller models while removing domain knowledge that contradicts task-oriented dialogues. Experiments show that our model outperforms strong TOD baselines on various downstream dialogue tasks and learns the intrinsic diversity of task-oriented dialogues.

## introduction many nlp applications frequently utilize pretrained language models (plms) @xcite , which are based on extensive general text corpora @xcite .these models are pre-trained in a self-supervised manner and then fine-tuned for supervised downstream tasks. the pretrain and finetune paradigm has significantly improved the performance of various downstream tasks. despite their success, most current research efforts focus on general documents such as wikipedia, which have a large linguistic gap with dialogues, particularly task-oriented dialogues. directly using these plms is not ideal and yields poor performance @xcite . compared to plain text, tod aims to help users accomplish specific tasks with explicit goals (e.g. restaurant reservation), belief states, and database i need restaurant in the east, could you recommend 1 with the food type? curry prince is available, would you like to try that ? i found 11.would you like to specify an area ? the meze bar restaurtant matches your criteria .would you like me to book it ? figure 1 : the same context may have multiple appropriate responses in a task-oriented dialogue, which we call one-to-many. despite previous tod plms have made remarkable progress. most work ignores the one-to-many property in the conversation where multiple responses can be appropriate under the same conversation context (shown in figure 1 ). our analysis shows that the lack of diversity in tod datasets is the main reason for this. specifically, (1) most tod datasets only provide a single response for the same dialogue history, and (2) the style of system responses in tod is often monotonous and dull. as a result, current tod plms capture only the most common dialogue information and ignore less frequent but still feasible user behaviors, which leads to duplicated and plain responses. large language models (llms) @xcite @xcite offer hope for addressing the problems mentioned above. llms have more parameters and are pretrained and fine-tuned on a richer and wider corpus @xcite @xcite . consequently, llms possess a broader general background knowledge, which enables them to generate more diverse and feasible responses. however, it should be noted that llms have not been specifically fine-tuned for task-oriented dialogue systems @xcite , resulting in a significant mismatch between their general knowledge and the domain knowledge required for task-oriented dialogue. furthermore, llms typically have billions of parameters, making them too expensive to deploy at scale because of the overwhelming computational requirements, as well as the cost of fine-tuning and inference @xcite . to address these issues, a natural approach is to distill the rich background and domain-specific knowledge required for tasks from llms into smaller and more efficient models. in this paper, we propose a new dialogue pretraining model, divtod, which enhances the ability of smaller models to model the intrinsic one-tomany diversity of human conversations by transferring rich general background knowledge and task-specific domain knowledge from llms. our framework consists of three core steps: (1) guiding llms to generate diverse system responses based on dialogue context in a "filling the blank" manner. (2) using an llm-based post-generation filter to align the generated responses with domain knowledge. (3) allowing small models to imitate llm's capabilities by observing diverse dialogues through self-training. we evaluated divtod on various task-oriented dialogue tasks, such as intent classification, dialogue state tracking, dialogue act prediction, and response selection. the results demonstrate that divtod consistently outperforms strong tod baselines in all scenarios, indicating its generalization capability. furthermore, we observed that divtod can capture a wider range of dialogue information and learn the intrinsic one-tomany diversity of tod. our contributions are: (1) we propose a framework that distills task-specific domain knowledge user： hi, could you get me a restaurant booking on the 8th please? sys(given response)：[masked] user： could you get me a reservation at p.f. chang's in corte madera at afternoon 12? prompt user： hi, could you get me a restaurant booking on the 8th please? sys(given response)：may i know if you have any particular cuisine or ambiance preference for the restaurant? and what time exactly would you like to dine in? user： could you get me a reservation at p.f. chang's in corte madera at afternoon 12? may i know if you have any particular cuisine or ambiance preference for the restaurant? and what time exactly would you like to dine in? prompt true/false diversified responses check result llm-teacher generating diversified responses aligning domain knowledge small student model self-training and rich general background knowledge of llms into smaller models. we use this framework to pre-train divtod and model the intrinsic one-tomany diversity of human conversations. (2) our divtod outperforms strong tod baselines on diverse downstream dialogue tasks. it also learns the intrinsic diversity of task-oriented dialogues 2 model ## conclusion we propose a new dialogue pre-training called di-vtod to diversify task-oriented dialogue representations by modeling the intrinsic one-to-many diversity of human conversations. divtod guides llms to transfer diverse background knowledge to smaller models while filtering domain knowledge that conflicts with task-oriented dialogues. our experiments on various task-oriented dialogue tasks show that divtod outperforms futuretod, tod-bert, dse, and other strong baselines. we plan to release all pre-trained models and code to facilitate future research. in the future, we hope to explore larger pre-trained models and more taskoriented dialogue corpora and extend similar ideas to generative dialogue models.