Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation

2026-04-29 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new framework, FairChart2Table, has been developed to analyze y-axis-related biases in Multimodal Language Models (MLMs) performing chart-to-table translation. This process converts chart images into structured tabular data, which is critical for MLMs to answer complex queries accurately. Researchers observed imbalances in the number of images across various y-axis information aspects within public chart datasets, leading to potential unintended biases and uneven MLM performance. The FairChart2Table framework was applied to five state-of-the-art models, revealing significant biases related to major tick value digit length, the number of major ticks, value range, and tick value format (e.g., abbreviation or scientific notation). Additionally, the number of legends or entities in chart images was found to impact MLM performance, and providing y-axis information via prompting can enhance some MLM performances.

Key takeaway

For AI Engineers developing or deploying Multimodal Language Models for chart-to-table translation, you should critically evaluate your training and validation datasets for y-axis biases. Understanding these biases, particularly regarding tick value formats and ranges, can help you anticipate performance discrepancies. Consider implementing explicit y-axis information in your prompting strategies to potentially improve model accuracy and fairness, especially when dealing with diverse chart types.

Key insights

Y-axis biases in chart datasets significantly impact Multimodal Language Model performance in chart-to-table translation.

Principles

• Dataset imbalances introduce unintended biases.
• Y-axis features influence MLM accuracy.
• Prompting can mitigate certain biases.

Method

FairChart2Table is a framework for systematically analyzing y-axis-related bias in MLMs by examining digit length, tick count, value range, and format.

In practice

• Inspect chart datasets for y-axis imbalances.
• Consider y-axis features in MLM evaluation.
• Experiment with y-axis prompting for MLMs.

Topics

• Multimodal Language Models
• Chart-to-Table Translation
• Y-Axis Bias Analysis
• FairChart2Table Framework
• Chart Legends Impact

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.