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James Ford

2 accepted papers

2025

Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations

COLING 2025main

We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable, or focus solely on data accuracy, neglecting the effectiven…

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2025

Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach

EMNLP 2025

Large language models can translate natural-language chart descriptions into runnable code, yet approximately 15% of the generated scripts still fail to execute, even after supervised fine-tuning and reinforcement learning. We investigate whether this persistent error rate stems from model limitatio