# P5: Plug-and-Play Persona Prompting for Personalized Response Selection

arXiv: https://arxiv.org/abs/2310.06390 · HTML: https://mlsanity.com/paper/2310.06390/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://mlsanity.com/paper/2008.02496/) (2020)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2004.03588/) (2020)
- [Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://mlsanity.com/paper/1806.09102/) (2018)
- [Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection](https://mlsanity.com/paper/2009.04703/) (2020)
- [Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances](https://mlsanity.com/paper/2204.10825/) (2022)
- [Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge](https://mlsanity.com/paper/2112.08619/) (2021)
- [Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/1908.05859/) (2019)
- [COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://mlsanity.com/paper/2205.00872/) (2022)
- [Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2105.09050/) (2021)
- [A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1906.04362/) (2019)
- [Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2004.14550/) (2020)
- [Syntax-based Deep Matching of Short Texts](https://mlsanity.com/paper/1503.02427/) (2015)
- [Content Selection Network for Document-grounded Retrieval-based Chatbots](https://mlsanity.com/paper/2101.08426/) (2021)
- [Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection](https://mlsanity.com/paper/2208.09601/) (2022)

## Built on by (most cited first, 8 of 8)

- [Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency](https://mlsanity.com/paper/2512.00461/) (2025)
- [Enhancing Jailbreak Attacks on LLMs via Persona Prompts](https://mlsanity.com/paper/2507.22171/) (2025)
- [Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models](https://mlsanity.com/paper/2505.17051/) (2025)
- [Exploring Persona Sentiment Sensitivity in Personalized Dialogue Generation](https://mlsanity.com/paper/2502.11423/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users](https://mlsanity.com/paper/2603.16120/) (2026)
- [Examining the Role of Relationship Alignment in Large Language Models](https://mlsanity.com/paper/2410.01708/) (2024)
- [Attribute-Based Activation Steering of LLMs for Group-Specific Explanation Generation](https://mlsanity.com/paper/2608.29215/) (2026)

## Abstract (arXiv)

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~footnotehttps://github.com/rungjoo/plug-and-play-prompt-persona.