用AI自动生成可交互的数据驱动论文，让读者随时查看结论背后的原始数据。
AI-Assisted Authoring for Transparent, Data-Driven Documents
- 基于大模型识别文本中可由数据生成的表述，自动转为可查询的代码片段。
- 在SciGen数据集上测试，GPT-4o对复杂表达式的生成准确率较高。
- 适合希望提升论文透明度的研究者，尤其关注可复现性与数据可信度。
我们提出「透明文档」概念，即一种基于网页的交互式学术文章，读者可通过悬停文本片段探索其背后的数据关联。本文构建了一个基于大语言模型的工具，用于创建此类文档，依托通用编程语言中的数据溯源技术。目标平台采用Fluid——一种具备溯源运行时的开源编程语言。该工具以代理形式辅助作者，识别文本中可由数据计算得出的内容，如从记录中选取的数值、求和平均等聚合结果、比较级（如‘优于’）和趋势形容词（如‘增长’）等定量或半定量表述，并尝试生成相应的Fluid查询语句。生成的表达式被嵌入网页，使静态文本变为可交互的数据驱动元素，支持揭示支撑自然语言陈述的原始数据。我们在SciGen数据集的一个子集上评估该方法，该数据集包含科学文章中的表格及其描述，并额外手工构造反事实测试用例以检验机器生成表达式的泛化能力。结果显示，GPT-4o通常能生成与人工标注答案在扩展意义上兼容的复合表达式。
原文摘要 · Abstract (English)
We introduce _transparent documents_, interactive web-based scholarly articles which allow readers to explore the relationship to the underlying data by hovering over fragments of text, and present an LLM-based tool for authoring transparent documents, building on recent developments in data provenance for general-purpose programming languages. As a target platform, our implementation uses Fluid, an open source programming language with a provenance-tracking runtime. Our agent-based tool supports a human author during the creation of transparent documents, identifying fragments of text which can be computed from data, such as numerical values selected from records or computed by aggregations like sum and mean, comparatives and superlatives like _better than_ and _largest_, trend-adjectives like _growing_, and similar quantitative or semi-quantitative phrases, and then attempts to synthesise a suitable Fluid query over the data which generates the target string. The resulting expression is inserted into the article's web page, turning the static text fragment into an interactable data-driven element able to reveal the data that underwrites the natural language claim. We evaluate our approach on a subset of SciGen, an open source dataset consisting of tables from scientific articles and their corresponding descriptions, which we extend with hand-generated counterfactual test cases to evaluate how well machine-generated expressions generalise. Our results show that gpt4o is often able to synthesise compound expressions extensionally compatible with our gold solutions.
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