SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models
<|reference_start|>SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models: Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning. To support our study, we created and contribute a novel Spatial Reasoning Characterization (SpaRC) framework and Spatial Reasoning Paths (SpaRP) datasets, to enable an in-depth understanding of the spatial relations and compositions as well as the usefulness of spatial reasoning chains. We found that all the state-of-the-art LLMs do not perform well on the datasets -- their performances are consistently low across different setups. The spatial reasoning capability improves substantially as model sizes scale up. Finetuning both large language models (e.g., Llama-2-70B) and smaller ones (e.g., Llama-2-13B) can significantly improve their F1-scores by 7--32 absolute points. We also found that the top proprietary LLMs still significantly outperform their open-source counterparts in topological spatial understanding and reasoning.<|reference_end|>
@article{rizvi2024sparc,
    title={SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation
  for Understanding Spatial Reasoning Capability of Large Language Models},
    author={Md Imbesat Hassan Rizvi, Xiaodan Zhu, Iryna Gurevych},
    journal={arXiv preprint arXiv:2406.04566},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04566},
    primaryClass={cs.CL cs.AI cs.LG}
}