Chat2VIS：使用ChatGPT、Codex和GPT-3大语言模型通过自然语言生成数据可视化
Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models
摘要：
数据可视化领域长期以来一直致力于设计用于直接从自然语言文本生成可视化的解决方案。对自然语言接口（NLI）的研究为这些技术的发展做出了贡献。然而，由于自然语言固有的模糊性，以及不清楚和写得很差的用户查询的结果，现有的语言模型在辨别用户意图方面存在问题，因此，可行的NLI的实现一直是具有挑战性的。这项研究没有追求开发新的语言模型迭代的通常路径，而是独特地提出利用ChatGPT和GPT-3等预训练大语言模型（LLM）的进步，将自由形式的自然语言直接转换为代码，以进行适当的可视化。本文提出了一种新的系统，Chat 2 VIS，它利用了LLM的功能，并演示了如何通过有效的提示工程，可以更有效地解决语言理解的复杂问题，从而比以前的方法更简单，更准确的端到端的解决方案。Chat 2 VIS表明，LLM与建议的提示一起提供了一种可靠的方法来渲染来自自然语言查询的可视化，即使查询是高度错误指定和指定不足。该解决方案还显著降低了NLI系统开发的成本，同时与使用手工语法规则和定制模型的传统NLP方法相比，获得了更高的可视化推理能力。本研究还介绍了如何以保护数据安全和隐私的方式构建LLM提示，同时可推广到不同的数据集。这项工作比较了GPT-3，Codex和ChatGPT在许多案例研究中的性能，并将其性能与先前的研究进行了对比。
英文摘要：
The field of data visualisation has long aimed to devise solutions for generating visualisations directly from natural language text. Research in Natural Language Interfaces (NLIs) has contributed towards the development of such techniques. However, the implementation of workable NLIs has always been challenging due to the inherent ambiguity of natural language, as well as in consequence of unclear and poorly written user queries which pose problems for existing language models in discerning user intent. Instead of pursuing the usual path of developing new iterations of language models, this study uniquely proposes leveraging the advancements in pre-trained large language models (LLMs) such as ChatGPT and GPT-3 to convert free-form natural language directly into code for appropriate visualisations. This paper presents a novel system, Chat2VIS, which takes advantage of the capabilities of LLMs and demonstrates how, with effective prompt engineering, the complex problem of language understanding can be solved more efficiently, resulting in simpler and more accurate end-to-end solutions than prior approaches. Chat2VIS shows that LLMs together with the proposed prompts offer a reliable approach to rendering visualisations from natural language queries, even when queries are highly misspecified and underspecified. This solution also presents a significant reduction in costs for the development of NLI systems, while attaining greater visualisation inference abilities compared to traditional NLP approaches that use hand-crafted grammar rules and tailored models. This study also presents how LLM prompts can be constructed in a way that preserves data security and privacy while being generalisable to different datasets. This work compares the performance of GPT-3, Codex and ChatGPT across a number of case studies and contrasts the performances with prior studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
