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~\footnote{this https URL}.

利用个人化引导的基于检索的聊天机器人在个性化对话中至关重要，但存在几个需要解决的挑战。为了解决这些挑战，我们提出了一种即插即用的个人化提示方法，该系统可以作为标准开放域聊天机器人使用，同时降低对个人化训练数据的依赖，提供更好的性能。这是首次使用提示序列解决个性化响应选择问题。