P5: Plug-and-Play Persona Prompting for Personalized Response Selection (2023)

Abstract: The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose a plug-and-play persona prompting method. Our system can function as a standard open-domain chatbot if persona information is not available. We demonstrate that this approach performs well in the zero-shot setting, which reduces the dependence on persona-ground training data. This makes it easier to expand the system to other languages without the need to build a persona-grounded corpus. Additionally, our model can be fine-tuned for even better performance. In our experiments, the zero-shot model improved the standard model by 7.71 and 1.04 points in the original persona and revised persona, respectively. The fine-tuned model improved the previous state-of-the-art system by 1.95 and 3.39 points in the original persona and revised persona, respectively. To the best of our knowledge, this is the first attempt to solve the problem of personalized response selection using prompt sequences. Our code is available on github.

## introduction designing a system that naturally communicates with humans is of great interest to researchers and is widely applied to services such as apple siri and amazon alexa. one of the critical algorithms of these services is multi-turn response selection, which selects the most appropriate response among many response candidates. selecting a personalized response with a customized chatbot is necessary for a more human-like conversational system. indeed, @xcite shows that dialog context alone is insufficient for response selection. @xcite released persona-chat where the speakers have each persona. the persona is expressed in multiple sentences, and they get to know each other through conversation. persona-chat can be used for research on personalized response generation and selection. however, the following challenges exist in developing personalized response selection for a real application. 1) building conversations based on persona is very expensive. persona-chat is data from the research environment in english, and persona-grounded corpus in other languages is difficult to access. that is, there is a challenge to build data for real applications. 2) in general domains, the persona may not need to be reflected. however, since previous approaches @xcite @xcite @xcite @xcite are trained in combination with personas, the model can select a response only given a persona. therefore, previous approaches always have the disadvantage of reflecting personal information. for example, when the persona is related to a favorite food, it is not helpful knowledge when answering the other topics (i.e., weather). ideally, a chatbot system needs the ability to consider persona as an option while maintaining standard response selection capabilities. we propose p5 (plug-and-play persona prompting for personalized response selection) to solve the above challenges. first, we assume that there is no expensive persona-based corpus. therefore, we can train only standard response selection models that do not consider persona. then, we show that the standard response selection model combined with persona prompting allows response selection to reflect persona, which is a zero-shot inference strategy. persona prompting improves the performance of standard response selection in persona-based conversations. also, the model uses persona prompting as optional information because it is a plug-and-play method. if no persona is given to the model, the model acts as a standard response selection model. so we can optionally combine model and persona. persona sentences to be used for prompting are selected by measuring the similarity to the response. we use a pre-trained model as our similarity model. only top-k persona sentences are used in order of highest similarity score. in addition, we introduce a zero-shot baseline sop (similarity of persona) based on the similarity score. to our best knowledge, previous studies only provide fine-tuned models. for comparison in these same experimental settings, we show the experimental results for fine-tuned p5. our method further improves the performance of the fine-tuned strategy as well as the zero-shot strategy. finetuned p5 achieves state-of-the-art, which proves that persona prompting is effective in learning the relationship between persona and context. we evaluate our methods on persona-chat @xcite and focus @xcite . persona-chat provides 19 negative responses and 1 positive response for personalized response selection. focus is given only one positive response as response candidates. therefore, we build the data by sampling 19 negative candidates. ## approach figure 1 shows the proposed approach. in the training phase, the model is trained to perform a multiturn response selection task with d train similar to @xcite ; @xcite , which is called standard response selection. since our goal is not to improve the performance of standard response selection, we do not use any particular strategy (i.e., post-training). the test phase consists of two steps. the first step is persona grounding corresponding to the response. the second step is calculating the matching score of (c, r, p) with the personaprompted standard response selection model. our approach improves the performance by combining the persona prompting with the standard response selection not trained with persona, which can also be utilized as a fine-tuned method. ## conclusion in this paper, we present a method called p5, which functions as a plug-and-play system that only incorporates persona when desired. our approach involves identifying related persona sentences through their similarity to a given response, and then adding these sentences as a prompt to the input. this allows the standard response selection model to better match context and response by taking into account the persona. we evaluate our method on two benchmark datasets using both fine-tuning and zero-shot settings. fine-tuned p5 outperforms previous studies by a significant margin. zero-shot p5 also effectively improves performance when compared to standard response selection models. even the zero-shot p5-large shows performance that is comparable to previous finetuning approaches. p5 is only evaluated using persona-based corpus, however, in real-world applications, persona information is not always available. therefore, it is important that the standard response selection model can be combined with persona in a dynamic manner. one way to achieve this is by only incorporating persona sentences that have a similarity score above a certain threshold. we plan to investigate other options for reflecting persona in future studies.