Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems (2023)

Abstract: Response generation is one of the critical components in task-oriented dialog systems. Existing studies have shown that large pre-trained language models can be adapted to this task. The typical paradigm of adapting such extremely large language models would be by fine-tuning on the downstream tasks which is not only time-consuming but also involves significant resources and access to fine-tuning data. Prompting (Schick and Schütze, 2020) has been an alternative to fine-tuning in many NLP tasks. In our work, we explore the idea of using prompting for response generation in task-oriented dialog systems. Specifically, we propose an approach that performs contextual dynamic prompting where the prompts are learnt from dialog contexts. We aim to distill useful prompting signals from the dialog context. On experiments with MultiWOZ 2.2 dataset (Zang et al., 2020), we show that contextual dynamic prompts improve response generation in terms of combined score (Mehri et al., 2019) by 3 absolute points, and an additional 17 points when dialog states are incorporated. Furthermore, we carried out human annotation on these conversations and found that agents which incorporate context are preferred over agents with vanilla prefix-tuning.

## introduction with the advent of large language models (llms), a vast majority of nlp tasks, including dialog systems, further fine-tune these lms for their downstream tasks. although these approaches provide substantial improvements over traditional taskspecific models @xcite @xcite , it is a time consuming process that also involves significant use of energy/resources in the form of compute. these approaches also require tuning and storing parameters for each downstream task. ## problem statement response generation is one of the tasks carried out in dialog systems usually in addition to dialog state tracking (dst). given a dialog context (previous turns between the system and the user) @xmath0, s 1 , ..., u n-1 , s n-1 ] and the current user utterance u n , the goal of response generation is to generate system response s n . note that in the actual task, we generate delexicalized system responses, given all the groundtruth previous turns as input, following previous works @xcite . techniques mentioned in @xcite rely on fully fine-tuning llms to carry out this task. in contrast, our approach builds on the prefix-tuning framework, but incorporates dialog context, c, as an additional signal for the prefix tokens. as a supplement to context c, we added dialog state information d (up to the current turn) to further help response generation. ## results as shown in ## conclusion in our work, we proposed an approach that performs contextual dynamic prompting where prompts are learnt from dialog contexts with the goal of distilling useful prompting signals. in our experiments, we showed that contextual dynamic prompts improve response generation in terms of combined score @xcite by 3 points, and by 20 points when dialog states are incorporated compared to the baseline. our technique does not expose the models to additional knowledge sources. human annotation on these conversations