mlsanityData to 7 Oct 2026

"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning

arXiv 2403.03102 ↗First posted 5 Mar 2024cs.CL cs.AIcited by 10

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Abstract (arXiv)

Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas. However, most existing approaches rely on pre-defined personal profiles, which are not only time-consuming and labor-intensive to create but also lack flexibility. We propose In-Dialogue Learning (IDL), a fine-tuning framework that enhances the ability of pre-trained large language models to leverage dialogue history to characterize persona for completing personalized dialogue generation tasks without pre-defined profiles. Our experiments on three datasets demonstrate that IDL brings substantial improvements, with BLEU and ROUGE scores increasing by up to 200% and 247%, respectively. Additionally, the results of human evaluations further validate the efficacy of our proposed method.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 35 of its 60 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

35 of 60 references.

References · 35

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23 more
  1. 2023Preference Ranking Optimization for Human Alignmentcited by 283
  2. 2017Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoderscited by 222
  3. 2019TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agentscited by 188
  4. 2019The Second Conversational Intelligence Challenge (ConvAI2)cited by 178
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  6. 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
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  10. 2019A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Datacited by 74
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  12. 2022Improving In-Context Few-Shot Learning via Self-Supervised Trainingcited by 56
  13. 2022Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generationcited by 47
  14. 2021One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profilescited by 39
  15. 2022Personalized Dialogue Generation with Persona-Adaptive Attentioncited by 24
  16. 2023Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Personacited by 24
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  18. 2022Improving Personality Consistency in Conversation by Persona Extendingcited by 17
  19. 2023Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialoguescited by 17
  20. 2023Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularizationcited by 17
  21. 2023CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignmentcited by 13
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 7.

10 papers.

  1. 2025Know You First and Be You Better: Modeling Human-Like User Simulators via Implicit Profilescited by 38
  2. 2025Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomycited by 3
  3. 2025Persona Alchemy: Designing, Evaluating, and Implementing Psychologically-Grounded LLM Agents for Diverse Stakeholder Representationcited by 2
  4. 2026HARMONI: Multimodal Personalization of Multi-User Human-Robot Interactions with LLMscited by 1
  5. 2026PersoDPO: Scalable Preference Optimization for Instruction-Adherent, Persona-Grounded Dialogue via Multi-LLM Evaluationcited by 0
  6. 2026Bridging Textual Profiles and Latent User Embeddings for Personalizationcited by 0
  7. 2026ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cuescited by 0
  8. 2026Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learningcited by 0
  9. 2026Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalizationcited by 0
  10. 2026Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiencycited by 0