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{https://github.com/rungjoo/plug-and-play-prompt-persona}.Comment: EMNLP 2023 main conferenc