Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (2023)

Abstract: Much work has been done to improve persona consistency by finetuning a pretrained dialogue model on high-quality human-annoated persona datasets. However, these methods still face the challenges of high cost and poor scalability. To this end, we propose a simple-yet-effective approach to significantly improve zero-shot persona consistency via in-context learning. Specifically, we first pre-train a persona-augmented dialogue generation model and then utilize in-context prompting mechanism to realize zero-shot persona customization. Experimental results demonstrate that our method can dramatically improve persona consistency without compromising coherence and informativeness in zero-shot settings.

## introduction endowing a dialog agent with consistent personas plays a very crucial role for build a more engaging and human-like chatbot. while large-scale pretrained dialog models have achieved great success @xcite @xcite @xcite , maintaining persona consistency remains unsatisfactory and challenging when applying these pretrained models to different scenarios with preset personas. traditional approaches for addressing this issue typically involve manually collecting persona dialogue datasets, such as personachat @xcite , and fine-tuning pre-trained dialogue models on these datasets @xcite . however, these methods face high costs for obtaining high-quality human-labeled persona datasets and scalability issues when encountering unseen persona information in the fine-tuned datasets @xcite to this end, we propose a novel in-context prompting learning mechanism to tackle zero-shot persona-based dialogue generation. by leveraging the pre-trained dialogue foundation model, we aim to generate persona-based dialogues without the need for expensive labeled data. prompt-based in-context learning @xcite has demonstrated its efficacy in few-shot or even zero-shot settings. in this work, we aim to implement a zero-shot persona dialogue generation mechanism using in-context prompt learning. however, we face two challenges: we would also like to highlight the practical value of our proposed work. in real application scenarios, the only cost of creating a new personalized chatbot is obtaining the corresponding persona prompts based on the prompt template, without the need for additional data annotation work. this significantly reduces the cost of creating a personalized chatbot. in summary, this work makes the following contributions: there exists much work on consistent personabased dialog generation. in particular, zhang et al. (2018) proposed the personachat dataset that has extensively promoted the development of this field where the crowd-workers are simply asked to chat with the other person naturally with the given personas. zheng et al. (2019) constructed a large-scale persona dataset based on structured persona knowledge with public weibo data. for improving persona consisitency, qian et al. (2018) proposed an explicit persona model to generate consistent responses for given profile information. liu et al. (2020) proposed p 2 bot to improve dialogue consistency by incorporating mutual persona perception. song et al. (2021) disentangled persona-based dialogue generation into consistency understanding and dialogue generation. cao et al. (2022) presented a model-agnostic data manipulation method for consistent persona generation. while these works have shown promising performance on preset personas in their respective datasets, they heavily rely on expensive humanlabeled datasets. moreover, customizing a chatbot with unseen personas in zero-shot settings remains challenging. to address these limitations, this paper proposes an in-context prompting learning mechanism to improve zero-shot persona consistency without the need for human-annotated data. ## in-context persona prompting learning we propose a novel in-context prompting learning mechanism for zero-shot personalized dialogue. the idea is simple yet highly effective. we format the preset persona information into multiple turns of a dialogue and place it at the beginning of the original dialogue context, as illustrated in figure 1 . this approach differentiates our method from @xcite , where the prompt consists of a single starting sentence. furthermore, our method also differs from most previous prompting methods in @xcite , where prompts are typically used to distinguish between different tasks. one of the key advantages of our approach is that it enables zero-shot persona customization without the need for annotating specific persona data. this distinguishes it from the dialogue prompting method proposed in @xcite , which requires fine-tuning on specific persona information. the detailed process of in-context persona prompting is as follows: ## experiments in this section, we will discuss the baselines, evaluation metrics, model comparisons, and results. ## conclusion in this paper, we propose a novel in-context prompting learning mechanism for zero-shot personalized dialogues. our framework is conceptually simple and practically effective, which can freely customize the pre-set personas of the chatbot. in addition, we train a new persona-augmented pre-trained generative dialogue model with the data merging the personal information of the dialogue speakers to enhance the in-context learning ability. experiments demonstrate that our model can dramatically improve the persona consistency from 45% to 85% of dialogues while maintaining coherence and informativeness in the zero-shot setting. in the future, we will apply our zero-shot framework to problems related to common sense knowledge reasoning of personas.