科学中的LLM：用于代码生成和数据分析
LLMs for Science: Usage for Code Generation and Data Analysis
AI总结：
本研究首次提供了LLM在科学研究过程中应用的实证证据，重点评估了其在代码生成和数据分析等软件工程用例中的表现，结果显示不同工具效果差异显著且存在输出完整性问题。
摘要：
大型语言模型（LLMs）被吹捧为能在当今工作生活的许多领域提高生产力。科学研究作为一个工作领域也不例外：基于LLM的工具协助科学家日常工作的潜力已成为跨学科高度讨论的话题。然而，我们仅处于这一研究主题的开端。LLM的潜力将如何在研究实践中实现仍不清楚。通过这项研究，我们提供了关于在研究过程中使用LLM的首批实证证据。我们调查了基于LLM工具在科学研究中的一系列用例，并进行了首次研究以评估当前工具在多大程度上是有帮助的。在本文中，我们特别报告了与软件工程相关的用例，例如生成应用程序代码和开发用于数据分析的脚本。虽然我们研究的是看似简单的用例，但不同工具的结果差异显著。我们的结果突出了基于LLM工具的总体前景，但我们也观察到了各种问题，特别是关于这些工具提供的输出的完整性。
英文摘要：
Large language models (LLMs) have been touted to enable increased productivity in many areas of today's work life. Scientific research as an area of work is no exception: the potential of LLM-based tools to assist in the daily work of scientists has become a highly discussed topic across disciplines. However, we are only at the very onset of this subject of study. It is still unclear how the potential of LLMs will materialise in research practice. With this study, we give first empirical evidence on the use of LLMs in the research process. We have investigated a set of use cases for LLM-based tools in scientific research, and conducted a first study to assess to which degree current tools are helpful. In this paper we report specifically on use cases related to software engineering, such as generating application code and developing scripts for data analytics. While we studied seemingly simple use cases, results across tools differ significantly. Our results highlight the promise of LLM-based tools in general, yet we also observe various issues, particularly regarding the integrity of the output these tools provide.
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