This minitrack is within the "Decision Analytics and Service Science" track of HICSS, which will occur January 5-8 2027 on the Big Island of Hawai'i.
The minitrack provides a focused exploration on applications of Natural Language Processing (NLP) and Large Language Models (LLMs) in the context of data analytics for system sciences. Aimed at the conference's emphasis on emerging managerial and organizational decision-making strategies in the digital age, this session has an emphasis on the use of text as the primary input to a wide variety of machine-learning algorithms and applications. Presentations will discuss how NLP and LLMs can be harnessed to enhance data analytics for system sciences.
Authors are invited to submit papers that delve into the practical applications and methods surrounding NLP and LLMs within the realms of data analytics, machine learning, business intelligence, and system sciences. The session seeks to provide clarity on the relevance of proposed research to the broader landscape of decision-making processes in contemporary digital environments. Descriptions of the development and deployment of NLP/LLM models are also welcome, such as those hosted on Google Cloud Platform, Amazon SageMaker or Oracle Cloud Infrastructure.
Here is a general list of topic areas for this minitrack:
* Business and Service Analytics: Showcasing practical applications of NLP and LLMs in business and service analytics, or providing insights into organizations gaining a competitive edge through intelligent data-driven decision-making.
* NLP Tasks: Relating to traditional NLP understanding tasks, such as sentiment analysis, named entity recognition, and part-of-speech tagging, and their application to system science
* Large Language Models: Using LLMs such as BERT, ChatGPT or Llama either to directly enable data analytics and system science applications, or in supporting roles such as dataset generation, text summarization, sentiment analysis, feature extraction, anomaly detection, or other data preprocessing tasks.
* Logistics and Supply Chain Management: Research work illustrating the impact of language technologies on optimizing logistics and supply chain management processes, fostering efficiency or increasing resilience.
* Other Applications: Papers focusing on the utilization of NLP and LLMs for other applications, elucidating how NLP technologies contribute to decision-making in a variety of contexts.
* Ethical Use of NLP & LLMs: Papers that highlight ways developers can work towards creating more fair and unbiased machine learning models, including bias detection or case studies that examine bias in machine learning models.