Summary of Grasp: a Grid-based Benchmark For Evaluating Commonsense Spatial Reasoning, by Zhisheng Tang et al.
GRASP: A Grid-Based Benchmark for Evaluating Commonsense Spatial Reasoning
by Zhisheng Tang, Mayank Kejriwal
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper proposes a new benchmark for evaluating the spatial reasoning abilities of Large Language Models (LLMs). Unlike existing benchmarks, GRASP (Grid-based Reasoning for Agent Spatial Planning) directly assesses an LLM’s ability to plan and solve specific spatial reasoning problems in grid-based environments. The benchmark consists of 16,000 scenarios with varying grid settings, energy distributions, agent starting positions, obstacles, and constraints. By comparing classic baseline approaches with advanced LLMs like GPT-3.5-Turbo, GPT-4o, and GPT-o1-mini, the results show that even these state-of-the-art models struggle to consistently achieve satisfactory solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well computers can solve problems involving spatial reasoning. Spatial reasoning is an important skill that humans use every day, like planning a route or figuring out where things are in space. The test, called GRASP, gives computers 16,000 different scenarios to try and solve. These scenarios have different things like energy sources, obstacles, and starting points for the computer’s “agent”. The results show that even very smart computer models don’t do well on this test. |
Keywords
» Artificial intelligence » Gpt
