As dialogue systems become increasingly important across various domains, a key challenge in persona-based dialogue is generating engaging and context-specific interactions while ensuring the model acts with a coherent personality. However, existing persona-based dialogue datasets lack explicit relations between persona sentences and responses, which makes it difficult for models to effectively capture persona information. To address these issues, we propose MoCoRP (Modeling Consistent Relations between Persona and Response), a framework that incorporates explicit relations into language models. MoCoRP leverages an NLI expert to explicitly extract the NLI relations between persona sentences and responses, enabling the model to effectively incorporate appropriate persona information from the context into its responses. We applied this framework to pre-trained models like BART and further extended it to modern large language models (LLMs) through alignment tuning. Experimental results on the public datasets ConvAI2 and MPChat demonstrate that MoCoRP outperforms existing baselines, achieving superior persona consistency and engaging, context-aware dialogue generation. Furthermore, our model not only excels in quantitative metrics but also shows significant improvements in qualitative aspects. These results highlight the effectiveness of explicitly modeling persona-response relations in persona-based dialogue. The source codes of MoCoRP are available at https://github.com/DMCB-GIST/MoCoRP.
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Ant Group（蚂蚁集团）；University of Electronic Science and Technology of China（电子科技大学）；Kunming University of Science and Technology（昆明理工大学）
Tsinghua University（清华大学）；Samsung Research China - Beijing (SRC-B)（三星中国研究院-北京（SRC-B））；NetEase Inc.（网易公司）
Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue cont…
From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
Department of Computer Science（计算机科学系）；Information Engineering, National Cheng Kung University, Taiwan（信息工程系，国立云林科技大学，台湾）；Department of Computer Science, National Tsing Hua University, Taiwan（计算机科学系，国立清华大学，台湾）
Korea University（高丽大学）；NCSOFT（NCSOFT公司）；Naver Corporation（Naver公司）
To build a conversational agent that interacts fluently with humans, previous studies blend knowledge or personal profile into the pre-trained language model. However, the model that considers knowledge and persona at the same time is still limited, leading to hallucination and a passive way of using personas. We propose an effective dialogue agent that grounds external knowledge and persona simultaneously. The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder. Then, our model generates the utterance with…
Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西湖大学工程学院）；Westlake University（西湖大学）；School of Early Childhood Education, Hangzhou Polytechnic（杭州职业技术学院幼儿教育学院）
MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；AI Lab, China Mobile Communication Group Tianjin Co., Ltd.（中国移动通信集团天津有限公司AI实验室）
Score Before You Speak: Improving Persona Consistency in Dialogue Generation using Response Quality Scores
School of Computer Science, University of Leeds（计算机科学学院，利兹大学）；Leeds Institute of Medical Education, School of Medicine, University of Leeds（利兹医学教育研究所，医学院，利兹大学）
Personalized Large Language Model Assistant with Evolving Conditional Memory
The Hong Kong Polytechnic University（香港理工大学）；INF Technology (Shanghai) co., Ltd.（INF科技（上海）有限公司）；Institute of Artificial Intelligence, Soochow University, China（苏州大学人工智能研究院）
The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
Department of Artificial Intelligence, Chung-Ang University, Seoul, 06974, Republic of Korea（Chung-Ang大学人工智能系）；Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada（不列颠哥伦比亚大学计算机科学系）；Van Lang University, Ho Chi Minh City, Vietnam（文-lang大学）
Persistent Personas? Role-Playing, Instruction Following, and Safety in Extended Interactions
University of Vienna, Faculty of Computer Science（维也纳大学计算机科学系）；Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学博士学院）；Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Vienna, Faculty of Philological and Cultural Studies（维也纳大学文学与文化研究系）
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；School of Computer Science, University of Chinese Academy of Sciences（中国科学院大学计算机科学学院）
Lived Experience in Dialogue: Co-designing Personalization in Large Language Models to Support Youth Mental Well-being
Delft University of Technology（代尔夫特理工大学）；University of Oulu（奥卢大学）；University of Oxford（牛津大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；University of Twente（特文特大学）；Erasmus University Rotterdam（鹿特丹伊拉斯姆斯大学）；Stanford University（斯坦福大学）
Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
Social chatbots, also known as chit-chat chatbots, evolve rapidly with large pretrained language models. Despite the huge progress, privacy concerns have arisen recently: training data of large language models can be extracted via model inversion attacks. On the other hand, the datasets used for training chatbots contain many private conversations between two individuals. In this work, we further investigate the privacy leakage of the hidden states of chatbots trained by language modeling which has not been well studied yet. We show that speakers' personas can be inferred through a simple neur…
Towards Stable and Personalised Profiles for Lexical Alignment in Spoken Human-Agent Dialogue
Centre for Language Studies, Centre for Language and Speech Technology, Radboud University,Nijmegen, The Netherlands（语言研究所以及语言与语音技术中心，拉德堡德大学，尼姆egen，荷兰）；Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands（行为科学研究所，拉德堡德大学，尼姆egen，荷兰）
The generation of personalized dialogue is vital to natural and human-like conversation. Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/personality of the interlocutor. As it is impractical to obtain the persona/personality representations for every interlocutor, recent works have explored the possibility of generating personalized dialogue by finetuning the model with dialogue examples corresponding to a given persona instead. However, in real-world implementations, a sufficient number …
