Proximity LLMs Encode Nearness From Text
A technical guide explains how large language models infer geographic proximity from text instead of GPS coordinates, detailing underlying embedding and trajectory-based similarity mechanisms. It surveys training signals, a taxonomy of spatial tasks, system architectures, and evaluation methods, citing benchmarks such as FloorplanQA (ICLR 2026) and SpatialMQA (ACL 2025) where top models scored 48.14% versus 98.40% human accuracy.
Key Points
- 1Explain embedding-based proximity: co-occurrence and trajectory-based similarities encode nearness without coordinates
- 2Highlight limits: benchmark gaps show models lag humans on spatial-relation tasks
- 3Recommend pipelines: amplify relational signals, use retrieval/fine-tuning, and add geospatial tools where needed
Scoring Rationale
Informative synthesis and conference-backed benchmark citations drive the score; modest novelty and limited empirical breakthroughs constrain transformative impact.
Sources
Public references used for this report.
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