Generative models (foundation models) such as LLMs (large language models) are having a large impact on multiple fields. In this work, we propose the use of such models for business decision making. In particular, we combine unstructured textual data sources (e.g., news data) with multiple foundation models (namely, GPT4, transformer-based Named Entity Recognition (NER) models and Entailment-based Zero-shot Classifiers (ZSC)) to derive IT (information technology) artifacts in the form of a (sequence of) signed business networks. We posit that such artifacts can inform business stakeholders about the state of the market and their own positioning as well as provide quantitative insights into improving their future outlook.

翻译：生成式模型（基础模型），如大型语言模型（LLMs），正在多个领域产生重大影响。本研究提出将此类模型应用于商业决策。具体而言，我们结合非结构化文本数据源（例如新闻数据）与多种基础模型（包括GPT4、基于Transformer的命名实体识别（NER）模型以及基于蕴含关系的零样本分类器（ZSC）），以构建信息技术（IT）工件，形式为（一系列）带符号的商业网络。我们认为，此类工件能够帮助商业利益相关者了解市场状况及其自身定位，并提供关于改善未来前景的定量见解。