mlsanityData to 14 Oct 2026

Building a Personalized Dialogue System with Prompt-Tuning

arXiv 2206.05399 ↗First posted 11 Jun 2022cs.CLcited by 9

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Abstract (arXiv)

Dialogue systems without consistent responses are not fascinating. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency. Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models. The results of automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses using less computational resources than fine-tuning.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 21 of its 23 references that are in the map. First posted on arXiv 11 Jun 2022.

Builds on

21 of 23 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  6. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
  7. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  8. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 828
  9. 2021GPT Understands, Toocited by 716
  10. 2021Multimodal Few-Shot Learning with Frozen Language Modelscited by 661
  11. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 652
  12. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
9 more
  1. 2020Recipes for building an open-domain chatbotcited by 593
  2. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 377
  3. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  4. 2016A Persona-Based Neural Conversation Modelcited by 223
  5. 2018Training Millions of Personalized Dialogue Agentscited by 126
  6. 2021What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformerscited by 76
  7. 2021BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Datacited by 75
  8. 2019Personalized Dialogue Generation with Diversified Traitscited by 74
  9. 2021Empirical Analysis of Training Strategies of Transformer-based Japanese Chit-chat Systemscited by 19

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 0; 2026: 1.

9 papers.

  1. 2024Hello Again! LLM-powered Personalized Agent for Long-term Dialoguecited by 123
  2. 2023Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackcited by 87
  3. 2024Are LLMs Robust for Spoken Dialogues?cited by 7
  4. 2024Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialoguecited by 7
  5. 2023Response Generation in Longitudinal Dialogues: Which Knowledge Representation Helps?cited by 6
  6. 2024Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversationscited by 5
  7. 2023Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generationcited by 4
  8. 2024User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuningcited by 3
  9. 2026HARMONI: Multimodal Personalization of Multi-User Human-Robot Interactions with LLMscited by 1