# LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming

arXiv: https://arxiv.org/abs/2306.08401 · HTML: https://mlsanity.com/paper/2306.08401/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [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)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [Beyond Goldfish Memory: Long-Term Open-Domain Conversation](https://mlsanity.com/paper/2107.07567/) (2021)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (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)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation](https://mlsanity.com/paper/2109.05729/) (2021)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2004.03588/) (2020)
- [Exploiting Persona Information for Diverse Generation of Conversational Responses](https://mlsanity.com/paper/1905.12188/) (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)
- [MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2106.01541/) (2021)
- [EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training](https://mlsanity.com/paper/2203.09313/) (2022)
- [Pchatbot: A Large-Scale Dataset for Personalized Chatbot](https://mlsanity.com/paper/2009.13284/) (2020)
- [Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs](https://mlsanity.com/paper/1709.04005/) (2017)
- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Consistent Dialogue Generation with Self-supervised Feature Learning](https://mlsanity.com/paper/1903.05759/) (2019)
- [Interview: A Large-Scale Open-Source Corpus of Media Dialog](https://mlsanity.com/paper/2004.03090/) (2020)

## Built on by (most cited first, 27 of 27)

- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://mlsanity.com/paper/2310.17976/) (2023)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding](https://mlsanity.com/paper/2502.10810/) (2025)
- [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024)
- [Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works](https://mlsanity.com/paper/2404.12726/) (2024)
- [Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?](https://mlsanity.com/paper/2404.12138/) (2024)
- [Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning](https://mlsanity.com/paper/2503.17662/) (2025)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [LiveStar: Live Streaming Assistant for Real-World Online Video Understanding](https://mlsanity.com/paper/2511.05299/) (2025)
- [How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation](https://mlsanity.com/paper/2502.14642/) (2025)
- [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://mlsanity.com/paper/2405.17974/) (2024)
- [Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency](https://mlsanity.com/paper/2512.00461/) (2025)
- [MINDECHO: Role-Playing Language Agents for Key Opinion Leaders](https://mlsanity.com/paper/2407.05305/) (2024)
- [If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs](https://mlsanity.com/paper/2503.23514/) (2025)
- [Evaluating LLM Adaptation to Sociodemographic Factors: User Profile vs. Dialogue History](https://mlsanity.com/paper/2505.21362/) (2025)
- [Exploring Persona Sentiment Sensitivity in Personalized Dialogue Generation](https://mlsanity.com/paper/2502.11423/) (2025)
- [AI Agents Alone Are Not (Yet) Sufficient for Social Simulation](https://mlsanity.com/paper/2603.00113/) (2026)
- [PerSHOP -- A Persian dataset for shopping dialogue systems modeling](https://mlsanity.com/paper/2401.00811/) (2024)
- [Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions](https://mlsanity.com/paper/2504.15300/) (2025)
- [Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation](https://mlsanity.com/paper/2510.16829/) (2025)
- [ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?](https://mlsanity.com/paper/2606.05553/) (2026)
- [LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams](https://mlsanity.com/paper/2606.17798/) (2026)
- [InteracVid: Building a Real Interactive Audio-Visual Response Dataset from Live-Chat Videos](https://mlsanity.com/paper/2608.01157/) (2026)
- [KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus](https://mlsanity.com/paper/2503.06899/) (2025)
- [Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding](https://mlsanity.com/paper/2606.06991/) (2026)
- [Scene-Aware Memory Discrimination: Deciding Which Personal Knowledge Stays](https://mlsanity.com/paper/2602.11607/) (2026)

## Abstract (arXiv)

Open-domain dialogue systems have made promising progress in recent years. While the state-of-the-art dialogue agents are built upon large-scale text-based social media data and large pre-trained models, there is no guarantee these agents could also perform well in fast-growing scenarios, such as live streaming, due to the bounded transferability of pre-trained models and biased distributions of public datasets from Reddit and Weibo, etc. To improve the essential capability of responding and establish a benchmark in the live open-domain scenario, we introduce the LiveChat dataset, composed of 1.33 million real-life Chinese dialogues with almost 3800 average sessions across 351 personas and fine-grained profiles for each persona. LiveChat is automatically constructed by processing numerous live videos on the Internet and naturally falls within the scope of multi-party conversations, where the issues of Who says What to Whom should be considered. Therefore, we target two critical tasks of response modeling and addressee recognition and propose retrieval-based baselines grounded on advanced techniques. Experimental results have validated the positive effects of leveraging persona profiles and larger average sessions per persona. In addition, we also benchmark the transferability of advanced generation-based models on LiveChat and pose some future directions for current challenges.