@inproceedings{wang-etal-2026-personatrace,
title = "{P}ersona{T}race: Synthesizing Realistic Digital Footprints with {LLM} Agents",
author = "Wang, Minjia and
Wang, Yunfeng and
Ma, Xiao and
Lv, Dexin and
Guo, Qifan and
Zheng, Lynn and
Wang, Benliang and
Wang, Lei and
Li, Jiannan and
Xing, Yongwei and
Xu, Junzhe and
Sun, Zheng",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.5/",
pages = "60--77",
ISBN = "979-8-89176-384-5",
abstract = "Digital footprints{---}records of individuals' interactions with digital systems{---}are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate that the generated dataset is more diverse and realistic than existing baselines. Moreover, models fine-tuned on our synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks."
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<abstract>Digital footprints—records of individuals’ interactions with digital systems—are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate that the generated dataset is more diverse and realistic than existing baselines. Moreover, models fine-tuned on our synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks.</abstract>
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%0 Conference Proceedings
%T PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents
%A Wang, Minjia
%A Wang, Yunfeng
%A Ma, Xiao
%A Lv, Dexin
%A Guo, Qifan
%A Zheng, Lynn
%A Wang, Benliang
%A Wang, Lei
%A Li, Jiannan
%A Xing, Yongwei
%A Xu, Junzhe
%A Sun, Zheng
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F wang-etal-2026-personatrace
%X Digital footprints—records of individuals’ interactions with digital systems—are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate that the generated dataset is more diverse and realistic than existing baselines. Moreover, models fine-tuned on our synthetic data outperform those trained on other synthetic datasets when evaluated on real-world out-of-distribution tasks.
%U https://aclanthology.org/2026.eacl-industry.5/
%P 60-77
Markdown (Informal)
[PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents](https://aclanthology.org/2026.eacl-industry.5/) (Wang et al., EACL 2026)
ACL
- Minjia Wang, Yunfeng Wang, Xiao Ma, Dexin Lv, Qifan Guo, Lynn Zheng, Benliang Wang, Lei Wang, Jiannan Li, Yongwei Xing, Junzhe Xu, and Zheng Sun. 2026. PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 60–77, Rabat, Morocco. Association for Computational Linguistics.