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

TL;DR

This paper demonstrates that overlaying a coordinate grid onto charts significantly improves data extraction accuracy in multimodal LLMs, outperforming semantic prompting methods.

Contribution

The study introduces a simple spatial priming technique using grid overlays that outperforms semantic methods for chart data extraction with current multimodal models.

Findings

Grid-based spatial priming reduces extraction error from 25.5% to 19.5%.

Semantic priming methods did not yield significant improvements.

Spatial context provision is more effective than semantic guidance for this task.

Abstract

The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (LLMs) show promise, their accuracy on non-standardized charts remains a challenge. This raises a key research question: what is the most effective strategy to improve model performance (high-level semantic priming) or low-level spatial priming? This paper presents a comparative investigation into these two distinct strategies. We describe our exploratory experiments with semantic methods, such as a two-stage metadata-first framework and Chain-of-Thought, which failed to produce a statistically significant improvement. In contrast, we present a simple but highly effective spatial priming method: overlaying a coordinate grid onto the chart image before analysis. Our quantitative experiment on a synthetic dataset demonstrates that this grid-based approach provides a statistically significant reduction in data extraction error (SMAPE reduced from 25.5% to 19.5%, p < 0.05) compared to a baseline. We conclude that for the current generation of multimodal models, providing explicit spatial context is a more effective and reliable strategy than high-level semantic guidance for this class of tasks.