Visualization Generation with Large Language Models: An Evaluation
CoRR(2024)
摘要
Analysts frequently need to create visualizations in the data analysis
process to obtain and communicate insights. To reduce the burden of creating
visualizations, previous research has developed various approaches for analysts
to create visualizations from natural language queries. Recent studies have
demonstrated the capabilities of large language models in natural language
understanding and code generation tasks. The capabilities imply the potential
of using large language models to generate visualization specifications from
natural language queries. In this paper, we evaluate the capability of a large
language model to generate visualization specifications on the task of natural
language to visualization (NL2VIS). More specifically, we have opted for
GPT-3.5 and Vega-Lite to represent large language models and visualization
specifications, respectively. The evaluation is conducted on the nvBench
dataset. In the evaluation, we utilize both zero-shot and few-shot prompt
strategies. The results demonstrate that GPT-3.5 surpasses previous NL2VIS
approaches. Additionally, the performance of few-shot prompts is higher than
that of zero-shot prompts. We discuss the limitations of GPT-3.5 on NL2VIS,
such as misunderstanding the data attributes and grammar errors in generated
specifications. We also summarized several directions, such as correcting the
ground truth and reducing the ambiguities in natural language queries, to
improve the NL2VIS benchmark.
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