# User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning

arXiv: https://arxiv.org/abs/2409.00887 · HTML: https://mlsanity.com/paper/2409.00887/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 2 Sep 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018)
- [Long Time No See! Open-Domain Conversation with Long-Term Persona Memory](https://mlsanity.com/paper/2203.05797/) (2022)
- [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://mlsanity.com/paper/2106.06169/) (2021)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://mlsanity.com/paper/1911.04700/) (2019)
- [Towards Persona-Based Empathetic Conversational Models](https://mlsanity.com/paper/2004.12316/) (2020)
- [Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://mlsanity.com/paper/2204.08128/) (2022)
- [A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation](https://mlsanity.com/paper/2204.09867/) (2022)
- [Personalized Dialogue Generation with Persona-Adaptive Attention](https://mlsanity.com/paper/2210.15088/) (2022)
- [Empirical Analysis of Training Strategies of Transformer-based Japanese Chit-chat Systems](https://mlsanity.com/paper/2109.05217/) (2021)
- [A Personalized Dialogue Generator with Implicit User Persona Detection](https://mlsanity.com/paper/2204.07372/) (2022)
- [Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning](https://mlsanity.com/paper/2302.03848/) (2023)
- [Building a Personalized Dialogue System with Prompt-Tuning](https://mlsanity.com/paper/2206.05399/) (2022)

## Built on by (most cited first, 3 of 3)

- [Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey](https://mlsanity.com/paper/2510.12178/) (2025)
- [HiMeS: Hippocampus-inspired Memory System for Personalized AI Assistants](https://mlsanity.com/paper/2601.06152/) (2026)
- [Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning](https://mlsanity.com/paper/2608.09507/) (2026)

## Abstract (arXiv)

This paper addresses user-specific dialogs. In contrast to previous research on personalized dialogue focused on achieving virtual user dialogue as defined by persona descriptions, user-specific dialogue aims to reproduce real-user dialogue beyond persona-based dialogue. Fine-tuning using the target user's dialogue history is an efficient learning method for a user-specific model. However, it is prone to overfitting and model destruction due to the small amount of data. Therefore, we propose a learning method for user-specific models by combining parameter-efficient fine-tuning with a pre-trained dialogue model that includes user profiles. Parameter-efficient fine-tuning adds a small number of parameters to the entire model, so even small amounts of training data can be trained efficiently and are robust to model destruction. In addition, the pre-trained model, which is learned by adding simple prompts for automatically inferred user profiles, can generate speech with enhanced knowledge of the user's profile, even when there is little training data during fine-tuning. In experiments, we compared the proposed model with large-language-model utterance generation using prompts containing users' personal information. Experiments reproducing real users' utterances revealed that the proposed model can generate utterances with higher reproducibility than the compared methods, even with a small model.