用多智能体分工降低图表生成错误率，执行成功率超95%。
Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach
- 分四步走：起草、执行、修复、判断，由多个智能体协作完成。
- 在Text2Chart31上错误率降至4.5%，比最强基线低近5个百分点。
- 适合关注图表质量、可访问性与真实语义的开发者和研究者。
大型语言模型可将自然语言描述转换为可运行代码，但即使经过监督微调和强化学习，约15%生成脚本仍无法执行。本文探究这一持续错误率是否源于模型能力不足或单次提示设计缺陷。为此，提出轻量级多智能体流水线，将起草、执行、修复与判断分离，仅使用现成GPT-4o-mini模型。在Text2Chart31基准上，系统在三次修复迭代内将执行错误率降至4.5%，优于最强微调基线近5个百分点，且计算开销显著更低。在ChartX基准上误差率为4.6%，展现出强泛化能力。当前执行成功率已基本解决，但人工审查发现100张样本中有6张存在幻觉，且基于LLM的可访问性审计显示，仅有33.3%（Text2Chart31）和7.2%（ChartX）的图表符合基础色盲友好规范。这表明未来工作应从执行可靠性转向提升图表美观性、语义准确性和可访问性。
原文摘要 · Abstract (English)
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 limitations or from reliance on a single-prompt design. To explore this, we propose a lightweight multi-agent pipeline that separates drafting, execution, repair, and judgment, using only an off-the-shelf GPT-4o-mini model. On the \textsc{Text2Chart31} benchmark, our system reduces execution errors to 4.5\% within three repair iterations, outperforming the strongest fine-tuned baseline by nearly 5 percentage points while requiring significantly less compute. Similar performance is observed on the \textsc{ChartX} benchmark, with an error rate of 4.6\%, demonstrating strong generalization. Under current benchmarks, execution success appears largely solved. However, manual review reveals that 6 out of 100 sampled charts contain hallucinations, and an LLM-based accessibility audit shows that only 33.3\% (\textsc{Text2Chart31}) and 7.2\% (\textsc{ChartX}) of generated charts satisfy basic colorblindness guidelines. These findings suggest that future work should shift focus from execution reliability toward improving chart aesthetics, semantic fidelity, and accessibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。