@inproceedings{virinchi-etal-2026-geoground,
title = "{G}eo{G}round: Uncertainty-Weighted Multi-Task Learning for Geo-Alignment and Address Defect Detection",
author = "Virinchi, Srinivas and
Gulati, Aman and
Saladi, Anoop",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.27/",
pages = "410--419",
ISBN = "979-8-89176-394-4",
abstract = "Address intelligence in e-commerce demands accurate geocoding and proactive defect detection under strict sub-50{~}ms latency constraints. These tasks are inherently coupled: precise spatial grounding provides a strong prior for defect propensity, yet prior approaches optimize them independently. While generative LLMs offer rich semantic representations, they lack spatial inductive bias and fail to meet real-time serving requirements. We introduce GeoGround, a multi-task learning framework that jointly models coordinate grounding and address defect detection. The model combines a hierarchical spatial grounding objective with Focal Loss for defect classification, using uncertainty-based task weighting to balance optimization under severe class imbalance. To strengthen supervision, we curate a large-scale noisy address dataset using LLM-assisted data construction, augmenting the training corpus with signals that are costly to obtain manually. GeoGround achieves 5.86$\times$ gains in address defect detection precision and up to 4.86$\times$ improvements in location prediction accuracy over strong encoder baselines, while remaining 75$\times$ more efficient than decoder LLMs such as Qwen2-1.5B. A two-week online A/B test in a large-scale delivery pipeline confirms real-world impact, yielding a 50 bps uplift in defect detection, a 40 bps gain in location prediction, and an estimated operational savings of {\$}3.09M annually."
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<abstract>Address intelligence in e-commerce demands accurate geocoding and proactive defect detection under strict sub-50 ms latency constraints. These tasks are inherently coupled: precise spatial grounding provides a strong prior for defect propensity, yet prior approaches optimize them independently. While generative LLMs offer rich semantic representations, they lack spatial inductive bias and fail to meet real-time serving requirements. We introduce GeoGround, a multi-task learning framework that jointly models coordinate grounding and address defect detection. The model combines a hierarchical spatial grounding objective with Focal Loss for defect classification, using uncertainty-based task weighting to balance optimization under severe class imbalance. To strengthen supervision, we curate a large-scale noisy address dataset using LLM-assisted data construction, augmenting the training corpus with signals that are costly to obtain manually. GeoGround achieves 5.86\times gains in address defect detection precision and up to 4.86\times improvements in location prediction accuracy over strong encoder baselines, while remaining 75\times more efficient than decoder LLMs such as Qwen2-1.5B. A two-week online A/B test in a large-scale delivery pipeline confirms real-world impact, yielding a 50 bps uplift in defect detection, a 40 bps gain in location prediction, and an estimated operational savings of $3.09M annually.</abstract>
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%0 Conference Proceedings
%T GeoGround: Uncertainty-Weighted Multi-Task Learning for Geo-Alignment and Address Defect Detection
%A Virinchi, Srinivas
%A Gulati, Aman
%A Saladi, Anoop
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F virinchi-etal-2026-geoground
%X Address intelligence in e-commerce demands accurate geocoding and proactive defect detection under strict sub-50 ms latency constraints. These tasks are inherently coupled: precise spatial grounding provides a strong prior for defect propensity, yet prior approaches optimize them independently. While generative LLMs offer rich semantic representations, they lack spatial inductive bias and fail to meet real-time serving requirements. We introduce GeoGround, a multi-task learning framework that jointly models coordinate grounding and address defect detection. The model combines a hierarchical spatial grounding objective with Focal Loss for defect classification, using uncertainty-based task weighting to balance optimization under severe class imbalance. To strengthen supervision, we curate a large-scale noisy address dataset using LLM-assisted data construction, augmenting the training corpus with signals that are costly to obtain manually. GeoGround achieves 5.86\times gains in address defect detection precision and up to 4.86\times improvements in location prediction accuracy over strong encoder baselines, while remaining 75\times more efficient than decoder LLMs such as Qwen2-1.5B. A two-week online A/B test in a large-scale delivery pipeline confirms real-world impact, yielding a 50 bps uplift in defect detection, a 40 bps gain in location prediction, and an estimated operational savings of $3.09M annually.
%U https://aclanthology.org/2026.acl-industry.27/
%P 410-419
Markdown (Informal)
[GeoGround: Uncertainty-Weighted Multi-Task Learning for Geo-Alignment and Address Defect Detection](https://aclanthology.org/2026.acl-industry.27/) (Virinchi et al., ACL 2026)
ACL