STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis.
Wenbin Li|Di Yao|Ruibo Zhao|Wenjie Chen|Zijie Xu|Chengxue Luo|Chang Gong|Quanliang Jing|Haining Tan|Jingping Bi
| Anthology ID: | DBLP:conf/www/00120ZC0L0JTB25 |
|---|---|
| Volume: | Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025 - 2 May 2025 |
| Year: | 2025 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 749-752 |
| URL: | https://doi.org/10.1145/3701716.3715293 |
| DOI: | https://doi.org/10.1145/3701716.3715293 |
| DBLP: | conf/www/00120ZC0L0JTB25 |
| BibTeX: |
@inproceedings{li-2025-stbench,
author = {Wenbin Li and
Di Yao and
Ruibo Zhao and
Wenjie Chen and
Zijie Xu and
Chengxue Luo and
Chang Gong and
Quanliang Jing and
Haining Tan and
Jingping Bi},
editor = {Guodong Long and
Michale Blumestein and
Yi Chang and
Liane Lewin-Eytan and
Zi Huang and
Elad Yom-Tov},
title = {{STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis}},
booktitle = {{Companion Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025 - 2 May 2025}},
pages = {749--752},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3701716.3715293},
doi = {https://doi.org/10.1145/3701716.3715293}
}