This study delves into the evolving role of generative Large Language Models (LLMs). Emphasizing the growing influence of these models in diverse domains, we employ a data-driven approach to analyze tasks assigned to generative LLMs.
Utilizing a dataset comprising over 3.8 million tweets, we identify and cluster 31,747 unique tasks, with a specific case study on ChatGPT. To reach this goal, the proposed method combines two Natural Language Processing (NLP) Techniques, Named Entity Recognition (NER) and BERTopic algorithms. Our findings reveal a wide spectrum of applications, from programming assistance to creative content generation, highlighting LLM’s versatility.
The analysis highlighted six emerging areas of application for ChatGPT: human resources, programming, social media, office automation, search engines, education.
The study also examines the implications of these findings for innovation management, proposing a research agenda to explore the intersection of the identified areas, with four stages of the innovation process: idea generation, screening/idea selection, development, and diffusion/sales/marketing.
This paper contributes to the understanding of user interactions with the emerging technology of generative LLMs and their potential impact on innovation, offering insights into future research challenges and opportunities.
Filippo Chiarello is currently an Assistant Professor (RTDB) at the School of Engineering, University of Pisa. He teaches Design and Innovation at the master’s degrees of Engineering Management and Data Science.
His research focuses on natural language processing techniques for studying technological innovation and HR-related phenomena. He is responsible for faculty development for his institution.
This seminar will be held in English.
More information: Elisa Villani (e.villani@unibo.it).