PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908881999888384 |
|---|---|
| author | Wang, Minjia Wang, Yunfeng Ma, Xiao Lv, Dexin Guo, Qifan Zheng, Lynn Wang, Benliang Wang, Lei Li, Jiannan Xing, Yongwei Xu, David Sun, Zheng |
| author_facet | Wang, Minjia Wang, Yunfeng Ma, Xiao Lv, Dexin Guo, Qifan Zheng, Lynn Wang, Benliang Wang, Lei Li, Jiannan Xing, Yongwei Xu, David Sun, Zheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11955 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents Wang, Minjia Wang, Yunfeng Ma, Xiao Lv, Dexin Guo, Qifan Zheng, Lynn Wang, Benliang Wang, Lei Li, Jiannan Xing, Yongwei Xu, David Sun, Zheng Computation and Language 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. |
| title | PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.11955 |