# GPT-5 Model Corrected GPT-4V's Chart Reading Errors, Not Prompting

arXiv: https://arxiv.org/abs/2510.06782 · HTML: https://mlsanity.com/paper/2510.06782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Reliable and Efficient Amortized Model-based Evaluation](https://mlsanity.com/paper/2503.13335/) (2025)
- [How Aligned are Human Chart Takeaways and LLM Predictions? A Case Study on Bar Charts with Varying Layouts](https://mlsanity.com/paper/2408.06837/) (2024)
- [CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models](https://mlsanity.com/paper/2505.17202/) (2025)
- [A Rigorous Behavior Assessment of CNNs Using a Data-Domain Sampling Regime](https://mlsanity.com/paper/2507.03866/) (2025)

## Abstract (arXiv)

We present a quantitative evaluation to understand the effect of zero-shot large-language model (LLMs) and prompting uses on chart reading tasks. We asked LLMs to answer 107 visualization questions to compare inference accuracies between the agentic GPT-5 and multimodal GPT-4V, for difficult image instances, where GPT-4V failed to produce correct answers. Our results show that model architecture dominates the inference accuracy: GPT5 largely improved accuracy, while prompt variants yielded only small effects. Pre-registration of this work is available here: https://osf.io/u78td/?view_only=6b075584311f48e991c39335c840ded3; the Google Drive materials are here:https://drive.google.com/file/d/1ll8WWZDf7cCNcfNWrLViWt8GwDNSvVrp/view.