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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…

Cited by 3SourcePDFScholar
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