Grid-Based Spatial Priming Boosts LLM Accuracy on Chart Data Extraction

A new study from arXiv (2605.08220) finds that spatial priming—overlaying a coordinate grid on chart images—significantly improves multimodal LLM accuracy in data extraction, outperforming semantic methods like Chain-of-Thought. The research compared high-level semantic priming strategies (metadata-first framework, Chain-of-Thought) with low-level spatial priming. Semantic approaches failed to yield statistically significant improvement. In contrast, the grid-based spatial method provided a statistically significant boost on a synthetic dataset. The work addresses the challenge of automated data extraction from non-standardized scientific charts for large-scale literature analysis.