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An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8
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Qualitative Spatial Reasoning (QSR) is well explored area of Commonsense Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently many claims have been made for the capabilities of Large Language Models (LLMs). In this paper we investigate the extent to which one particular LLM can perform classical qualitative spatial reasoning tasks on the mereotopological calculus, RCC-8.
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Cited by 3 Pith papers
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Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
Large language models, especially GPT-4 with few-shot prompts, can classify topological spatial relations between WKT-encoded geometries with roughly 0.6 to 0.66 accuracy, though errors cluster near conceptually simil...
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Can Large Language Models Reason about the Region Connection Calculus?
State-of-the-art LLMs perform poorly on RCC-8 composition, preferred composition, and conceptual neighbourhood tasks, only modestly above chance.
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Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs
A DSPy-orchestrated LLM plus Answer Set Programming pipeline reports 82% average accuracy on StepGame and 69% on SparQA, well above direct prompting baselines.
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