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.

翻译：大型语言模型（LLMs）已被宣称能提升当今工作生活多领域生产力。科学研究作为工作领域之一亦不例外：基于LLM的工具辅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，我们仍处于该研究方向的起步阶段，尚不明确LLM的潜力如何在科研实践中具体显现。本研究首次为LLM在研究过程中的应用提供了经验证据。我们探究了科学研究中基于LLM工具的一系列应用场景，并开展初步研究评估当前工具的实用程度。本文重点报告与软件工程相关的场景，例如生成应用程序代码和开发数据分析脚本。尽管我们研究的场景看似简单，但不同工具的结果存在显著差异。研究结果凸显了基于LLM工具的总体前景，同时也观察到诸多问题，尤其涉及这些工具输出内容的完整性。