WHAT, WHEN, and HOW to Ground: Designing User Persona-Aware Conversational Agents for Engaging Dialogue
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Abstract (arXiv)
This paper presents a method for building a personalized open-domain dialogue system to address the WWH (WHAT, WHEN, and HOW) problem for natural response generation in a commercial setting, where personalized dialogue responses are heavily interleaved with casual response turns. The proposed approach involves weighted dataset blending, negative persona information augmentation methods, and the design of personalized conversation datasets to address the challenges of WWH in personalized, open-domain dialogue systems. Our work effectively balances dialogue fluency and tendency to ground, while also introducing a response-type label to improve the controllability and explainability of the grounded responses. The combination of these methods leads to more fluent conversations, as evidenced by subjective human evaluations as well as objective evaluations.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 6 Jun 2023.
Builds on
14 of 34 references.
References · 14
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 820
- 2018Wizard of Wikipedia: Knowledge-Powered Conversational agentscited by 530
- 2020Towards a Human-like Open-Domain Chatbotcited by 467
- 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversationcited by 266
- 2020Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skillscited by 130
- 2022ProsocialDialog: A Prosocial Backbone for Conversational Agentscited by 109
- 2022Long Time No See! Open-Domain Conversation with Long-Term Persona Memorycited by 107
- 2020You Impress Me: Dialogue Generation via Mutual Persona Perceptioncited by 85
- 2022Keep Me Updated! Memory Management in Long-term Conversationscited by 62
- 2019Exploiting Persona Information for Diverse Generation of Conversational Responsescited by 58
- 2019Guiding Variational Response Generator to Exploit Personacited by 25
Built on by
Citing papers per year: 2024: 6; 2025: 3; 2026: 1.
10 papers.
- 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Surveycited by 56
- 2024Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluationscited by 19
- 2024Social Skill Training with Large Language Modelscited by 13
- 2024SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Scriptcited by 6
- 2024FairMonitor: A Dual-framework for Detecting Stereotypes and Biases in Large Language Modelscited by 5
- 2024Apollonion: Profile-centric Dialog Agentcited by 4
- 2025Post Persona Alignment for Multi-Session Dialogue Generationcited by 3
- 2026Latent Preference Modeling for Multi-Session Personalized Tool Callingcited by 2
- 2024Personalized LLM for Generating Customized Responses to the Same Query from Different Userscited by 1
- 2025MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational Agentscited by 0
- 2026Latent Preference Modeling for Multi-Session Personalized Tool Calling20 Apr 2026
- 2025Post Persona Alignment for Multi-Session Dialogue Generation13 Jun 2025
- 2025Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey28 Mar 2025
- 2025MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational Agents9 Feb 2025
- 2024Personalized LLM for Generating Customized Responses to the Same Query from Different Users16 Dec 2024
- 2024SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Script28 Oct 2024
- 2024Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations28 May 2024
- 2024FairMonitor: A Dual-framework for Detecting Stereotypes and Biases in Large Language Models6 May 2024
- 2024Apollonion: Profile-centric Dialog Agent10 Apr 2024
- 2024Social Skill Training with Large Language Models5 Apr 2024