Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses

• URL: http://arxiv.org/abs/2508.05009v1
• Date: Thu, 07 Aug 2025 03:44:20 GMT
• Title: Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses
• Authors: Bin Han, Robert Wolfe, Anat Caspi, Bill Howe,
• Abstract summary: We explore the application of large language models (LLMs) to empower domain experts in integrating large, heterogeneous, and noisy urban spatial datasets.<n>We show that while LLMs exhibit spatial reasoning capabilities, they struggle to connect the macro-scale environment with the relevant computational geometry tasks.<n>We then adapt a review-and-refine method, which proves remarkably effective in correcting erroneous initial responses while preserving accurate responses.
• Score: 11.330846631937671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore the application of large language models (LLMs) to empower domain experts in integrating large, heterogeneous, and noisy urban spatial datasets. Traditional rule-based integration methods are unable to cover all edge cases, requiring manual verification and repair. Machine learning approaches require collecting and labeling of large numbers of task-specific samples. In this study, we investigate the potential of LLMs for spatial data integration. Our analysis first considers how LLMs reason about environmental spatial relationships mediated by human experience, such as between roads and sidewalks. We show that while LLMs exhibit spatial reasoning capabilities, they struggle to connect the macro-scale environment with the relevant computational geometry tasks, often producing logically incoherent responses. But when provided relevant features, thereby reducing dependence on spatial reasoning, LLMs are able to generate high-performing results. We then adapt a review-and-refine method, which proves remarkably effective in correcting erroneous initial responses while preserving accurate responses. We discuss practical implications of employing LLMs for spatial data integration in real-world contexts and outline future research directions, including post-training, multi-modal integration methods, and support for diverse data formats. Our findings position LLMs as a promising and flexible alternative to traditional rule-based heuristics, advancing the capabilities of adaptive spatial data integration.

Related papers

• Warehouse Spatial Question Answering with LLM Agent [18.821295196340383]
  We propose a LLM agent system with strong and advanced spatial reasoning ability.<n>Our system integrates multiple tools that allow the LLM agent to conduct spatial reasoning and API tools interaction.<n>Our system achieves high accuracy and efficiency in tasks such as object retrieval, counting, and distance estimation.
  arXiv  Detail & Related papers  (2025-07-14T20:05:55Z)
• Can LLMs Learn to Map the World from Local Descriptions? [50.490593949836146]
  This study investigates whether Large Language Models (LLMs) can construct coherent global spatial cognition.<n> Experiments conducted in a simulated urban environment demonstrate that LLMs exhibit latent representations aligned with real-world spatial distributions.
  arXiv  Detail & Related papers  (2025-05-27T08:22:58Z)
• SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding [64.15606979785355]
  Multimodal large language models (MLLMs) have achieved impressive success in question-answering tasks, yet their capabilities for spatial understanding are less explored.<n>This work investigates a critical question: do existing MLLMs possess 3D spatial perception and understanding abilities?
  arXiv  Detail & Related papers  (2025-05-22T17:59:03Z)
• EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks [24.41705039390567]
  EmbodiedVSR (Embodied Visual Spatial Reasoning) is a novel framework that integrates dynamic scene graph-guided Chain-of-Thought (CoT) reasoning.<n>Our method enables zero-shot spatial reasoning without task-specific fine-tuning.<n>Experiments demonstrate that our framework significantly outperforms existing MLLM-based methods in accuracy and reasoning coherence.
  arXiv  Detail & Related papers  (2025-03-14T05:06:07Z)
• Exploring Language Model Generalization in Low-Resource Extractive QA [57.14068405860034]
  We investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift.<n>We devise a series of experiments to explain the performance gap empirically.
  arXiv  Detail & Related papers  (2024-09-27T05:06:43Z)
• Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories [1.3658544194443192]
  This research focuses on assessing the ability of AI foundation models in representing the trajectories of movements. We utilize one of the large language models (LLMs) to encode the string format of trajectories and then evaluate the effectiveness of the LLM-based representation for trajectory data analysis.
  arXiv  Detail & Related papers  (2024-08-31T02:57:25Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models [70.01883340129204]
  spatial reasoning is a crucial component of both biological and artificial intelligence. We present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning.
  arXiv  Detail & Related papers  (2024-06-07T01:06:34Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases [9.610231090476857]
  Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language. This paper is an experimental investigation aimed at uncovering the challenges that LMs encounter when tasked with knowledge base question answering (KBQA) Our experiments reveal that even when employed with our proposed data augmentation techniques, advanced small and large language models exhibit poor performance in various dimensions.
  arXiv  Detail & Related papers  (2023-09-15T12:06:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.