The exponential growth of digital content has generated massive textual
datasets, necessitating advanced analytical approaches. Large Language Models
(LLMs) have emerged as tools capable of processing and extracting insights from
massive unstructured textual datasets. However, how to leverage LLMs for
text-based Information Systems (IS) research is currently unclear. To assist IS
research in understanding how to operationalize LLMs, we propose a Text
Analytics for Information Systems Research (TAISR) framework. Our proposed
framework provides detailed recommendations grounded in IS and LLM literature
on how to conduct meaningful text-based IS research. We conducted three case
studies in business intelligence using our TAISR framework to demonstrate its
application across several IS research contexts. We also outline potential
challenges and limitations in adopting LLMs for IS. By offering a systematic
approach and evidence of its utility, our TAISR framework contributes to future
IS research streams looking to incorporate powerful LLMs for text analytics.

数字内容的指数级增长引发了大规模文本数据集，需要先进的分析方法。大型语言模型作为处理和提取海量非结构化文本数据集的工具已经出现。为了帮助信息系统研究了解如何操作化大型语言模型，我们提出了一个用于信息系统研究的文本分析框架（TAISR）。我们的框架为如何进行有意义的基于文本的信息系统研究提供了详细的建议，这些建议基于信息系统和大型语言模型的文献。我们在商业智能领域进行了三个案例研究，用我们的 TAISR 框架来展示其在几个信息系统研究背景下的应用。我们还概述了采用大型语言模型进行信息系统研究可能面临的挑战和局限。通过提供一种系统化的方法并证明其实用性，我们的 TAISR 框架有助于未来将强大的大型语言模型添加到文本分析的信息系统研究流程中。

大型语言模型用于进行高级文本分析信息系统研究

Large Language Models for Conducting Advanced Text Analytics Information  Systems Research

Managing the data for Information Retrieval (IR) experiments can be
challenging. Dataset documentation is scattered across the Internet and once
one obtains a copy of the data, there are numerous different data formats to
work with. Even basic formats can have subtle dataset-specific nuances that
need to be considered for proper use. To help mitigate these challenges, we
introduce a new robust and lightweight tool (ir_datasets) for acquiring,
managing, and performing typical operations over datasets used in IR. We
primarily focus on textual datasets used for ad-hoc search. This tool provides
both a Python and command line interface to numerous IR datasets and
benchmarks. To our knowledge, this is the most extensive tool of its kind.
Integrations with popular IR indexing and experimentation toolkits demonstrate
the tool's utility. We also provide documentation of these datasets through the
ir_datasets catalog: this https URL The catalog acts as a hub for
information on datasets used in IR, providing core information about what data
each benchmark provides as well as links to more detailed information. We
welcome community contributions and intend to continue to maintain and grow
this tool.

介绍一个 names 为 ir_datasets 的工具，使 Information Retrieval (IR) 实验的数据处理更加容易。该工具提供 Python 和命令行接口，处理文本数据集用于全文搜索，并通过 ir_datasets 目录提供数据集的信息以及各项基准数据提供的核心信息和链接。