The Natural Language to Visualization (NL2Vis) task aims to transform
natural-language descriptions into visual representations for a grounded table,
enabling users to gain insights from vast amounts of data. Recently, many deep
learning-based approaches have been developed for NL2Vis. Despite the
considerable efforts made by these approaches, challenges persist in
visualizing data sourced from unseen databases or spanning multiple tables.
Taking inspiration from the remarkable generation capabilities of Large
Language Models (LLMs), this paper conducts an empirical study to evaluate
their potential in generating visualizations, and explore the effectiveness of
in-context learning prompts for enhancing this task. In particular, we first
explore the ways of transforming structured tabular data into sequential text
prompts, as to feed them into LLMs and analyze which table content contributes
most to the NL2Vis. Our findings suggest that transforming structured tabular
data into programs is effective, and it is essential to consider the table
schema when formulating prompts. Furthermore, we evaluate two types of LLMs:
finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5),
against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench).
The experimental results reveal that LLMs outperform baselines, with
inference-only models consistently exhibiting performance improvements, at
times even surpassing fine-tuned models when provided with certain few-shot
demonstrations through in-context learning. Finally, we analyze when the LLMs
fail in NL2Vis, and propose to iteratively update the results using strategies
such as chain-of-thought, role-playing, and code-interpreter. The experimental
results confirm the efficacy of iterative updates and hold great potential for
future study.

使用大型语言模型进行自然语言到可视化的转换任务，在分析如何将结构化表格数据转化为 LLM 所需的顺序文本提示时，发现将结构化表格数据转化为程序是有效的，并且在制定提示时考虑表格模式是必要的。同时，通过对比实验发现，LLM 在 NL2Vis 任务中优于基线方法，并且推理模型在提供少量示范的情况下通过上下文学习能够进一步改善性能，时而甚至超过微调模型。最后，分析 LLM 在 NL2Vis 任务中失败的情况，并提出了循环更新的策略，通过链式思维、角色扮演和代码解释等方法来迭代更新结果，实验证实了这种迭代更新的有效性，具有广阔的未来研究潜力。