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SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning

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arxiv 2104.05832 v1 pith:KHISQ6YZ submitted 2021-04-12 cs.CL cs.AI

Roshanak Mirzaee , Hossein Rajaby Faghihi , Qiang Ning , Parisa Kordjmashidi This is my paper
classification cs.CLcs.AI
keywords spatialreasoningautomaticallybenchmarklanguagemodelstextwork
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper proposes a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work and is challenging for state-of-the-art language models (LM). We propose a distant supervision method to improve on this task. Specifically, we design grammar and reasoning rules to automatically generate a spatial description of visual scenes and corresponding QA pairs. Experiments show that further pretraining LMs on these automatically generated data significantly improves LMs' capability on spatial understanding, which in turn helps to better solve two external datasets, bAbI, and boolQ. We hope that this work can foster investigations into more sophisticated models for spatial reasoning over text.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GS-QA: A Benchmark for Geospatial Question Answering

    cs.DB 2026-05 unverdicted novelty 7.0 of 10

    GS-QA is a new benchmark of 2,800 QA pairs on 28 templates using OSM and Wikipedia data to evaluate LLMs on spatial predicates, multi-source reasoning, and diverse answer types including distances and counts.

  2. SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Dense scene-graph-grounded rewards let a 7B multimodal LLM trained on 7K synthetic questions beat SFT and sparse-RL baselines and outscore GPT-4o on average across 12 spatial/real-world benchmarks.

  3. IMoRe: Implicit Program-Guided Reasoning for Human Motion Q&A

    cs.CV 2025-08 conditional novelty 6.0 of 10

    IMoRe couples a MAC-style memory network with program-function embeddings and multi-level ViT motion features to reach state-of-the-art accuracy on Babel-QA and a new HuMMan-QA benchmark.

  4. Learning to Draw ASCII Improves Spatial Reasoning in Language Models

    cs.AI 2026-04 unverdicted novelty 5.0 of 10

    Training LLMs on text-to-ASCII spatial layout construction improves text-only spatial reasoning and transfers to external benchmarks.

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