@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 (Volume 6: Industry Track)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-form-platform/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."
}