GeoGround: Uncertainty-Weighted Multi-Task Learning for Geo-Alignment and Address Defect Detection

Srinivas Virinchi, Aman Gulati, Anoop Saladi

Keywords: Multi-Task, Address Embeddings, Contrastive Learning

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.

Submission Type: Deployed

Submission Number: 94