Summary of Sparc and Sparp: Spatial Reasoning Characterization and Path Generation For Understanding Spatial Reasoning Capability Of Large Language Models, by Md Imbesat Hassan Rizvi et al.
SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models
by Md Imbesat Hassan Rizvi, Xiaodan Zhu, Iryna Gurevych
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the capability of current large language models (LLMs) on spatial reasoning, a crucial aspect of both biological and artificial intelligence. The researchers created the Spatial Reasoning Characterization (SpaRC) framework and Spatial Reasoning Paths (SpaRP) datasets to assess the performance of state-of-the-art LLMs on spatial relations, compositions, and chains. Surprisingly, all LLMs struggled with the tasks, exhibiting low performances across different setups. However, as model sizes increased, their spatial reasoning capabilities improved. Finetuning both large and small models led to significant F1-score improvements (7-32 absolute points). Moreover, proprietary LLMs outperformed open-source counterparts in topological spatial understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looked at how well big language models can understand and solve problems that involve space and relationships between objects. The researchers made new tools called SpaRC and SpaRP to test the models’ abilities. They found that even the best models didn’t do very well on these tasks, but bigger models did a bit better. When they fine-tuned some of the models, they got a lot better at solving these problems. Interestingly, the most advanced models made by companies were still much better than the ones developed by researchers. |
Keywords
» Artificial intelligence » F1 score
