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arXiv CS.AI
5/12/2026
Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction

Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction

Short summary

Researchers tested semantic vs. spatial priming for improving multimodal LLM performance on chart data extraction. Semantic methods (metadata-first, Chain-of-Thought) failed, but overlaying coordinate grids on images reduced extraction error from 25.5% to 19.5% (p < 0.05). Explicit spatial context proved more effective than high-level semantic guidance for current-generation multimodal models on this task.

  • •Semantic priming strategies (metadata-first, Chain-of-Thought) did not improve multimodal LLM chart extraction accuracy
  • •Grid overlay method reduced SMAPE error from 25.5% to 19.5% with statistical significance (p < 0.05)
  • •For current multimodal models, spatial context outperforms semantic guidance on structured data extraction

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