@inproceedings{yang-etal-2026-mcl,
title = "{MCL}: Meta-Collaborative Learning for Generalized Personalized Dialogue Generation",
author = "Yang, Muchen and
Gao, Chongming and
Zhu, Fengbin and
Gan, Weinan and
Shi, Wentao and
Zhang, Jizhi and
Dai, Quanyu and
Liu, Yong and
Li, Yangyang and
Feng, Fuli",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1856/",
pages = "39630--39642",
ISBN = "979-8-89176-462-0",
abstract = "Large language models (LLMs) have greatly advanced conversational systems, making Personalized Dialogue Generation (PDG) a core direction for tailoring responses to individual users. However, retrieval-augmented and user-parameterization approaches typically rely on dense user-level persona information and consistent domain-level persona distributions, leading to two key challenges for scalable PDG: sparse personas of unseen users hinder personalization, while domain-bound user parameters require costly retraining across dialogue scenes. Therefore, we argue that these limitations originate from an over-reliance on static data fitting, as opposed to mastering atomic personalization capabilities. To this end, we propose Meta-Collaborative Learning (MCL), a novel learning paradigm that proactively supplements cues via \textit{Neighbor Identifier} and \textit{Booster}, and dynamically aligns responses to preferences by \textit{Generator}. By decomposing PDG into synergistic modules with bi-level joint optimization, MCL internalizes universal personalization skills for acquiring and leveraging contextual signals across diverse users and domains. Extensive experiments validate that MCL delivers robust personalization under practical unseen-user and cross-domain settings."
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<abstract>Large language models (LLMs) have greatly advanced conversational systems, making Personalized Dialogue Generation (PDG) a core direction for tailoring responses to individual users. However, retrieval-augmented and user-parameterization approaches typically rely on dense user-level persona information and consistent domain-level persona distributions, leading to two key challenges for scalable PDG: sparse personas of unseen users hinder personalization, while domain-bound user parameters require costly retraining across dialogue scenes. Therefore, we argue that these limitations originate from an over-reliance on static data fitting, as opposed to mastering atomic personalization capabilities. To this end, we propose Meta-Collaborative Learning (MCL), a novel learning paradigm that proactively supplements cues via Neighbor Identifier and Booster, and dynamically aligns responses to preferences by Generator. By decomposing PDG into synergistic modules with bi-level joint optimization, MCL internalizes universal personalization skills for acquiring and leveraging contextual signals across diverse users and domains. Extensive experiments validate that MCL delivers robust personalization under practical unseen-user and cross-domain settings.</abstract>
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%0 Conference Proceedings
%T MCL: Meta-Collaborative Learning for Generalized Personalized Dialogue Generation
%A Yang, Muchen
%A Gao, Chongming
%A Zhu, Fengbin
%A Gan, Weinan
%A Shi, Wentao
%A Zhang, Jizhi
%A Dai, Quanyu
%A Liu, Yong
%A Li, Yangyang
%A Feng, Fuli
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yang-etal-2026-mcl
%X Large language models (LLMs) have greatly advanced conversational systems, making Personalized Dialogue Generation (PDG) a core direction for tailoring responses to individual users. However, retrieval-augmented and user-parameterization approaches typically rely on dense user-level persona information and consistent domain-level persona distributions, leading to two key challenges for scalable PDG: sparse personas of unseen users hinder personalization, while domain-bound user parameters require costly retraining across dialogue scenes. Therefore, we argue that these limitations originate from an over-reliance on static data fitting, as opposed to mastering atomic personalization capabilities. To this end, we propose Meta-Collaborative Learning (MCL), a novel learning paradigm that proactively supplements cues via Neighbor Identifier and Booster, and dynamically aligns responses to preferences by Generator. By decomposing PDG into synergistic modules with bi-level joint optimization, MCL internalizes universal personalization skills for acquiring and leveraging contextual signals across diverse users and domains. Extensive experiments validate that MCL delivers robust personalization under practical unseen-user and cross-domain settings.
%U https://aclanthology.org/2026.findings-emnlp.1856/
%P 39630-39642
Markdown (Informal)
[MCL: Meta-Collaborative Learning for Generalized Personalized Dialogue Generation](https://aclanthology.org/2026.findings-emnlp.1856/) (Yang et al., Findings 2026)
ACL
- Muchen Yang, Chongming Gao, Fengbin Zhu, Weinan Gan, Wentao Shi, Jizhi Zhang, Quanyu Dai, Yong Liu, Yangyang Li, and Fuli Feng. 2026. MCL: Meta-Collaborative Learning for Generalized Personalized Dialogue Generation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 39630–39642, Budapest, Hungary. Association for Computational Linguistics.