Tracing the Influence of Large Language Models across the Most Impactful Scientific Works

Abstract

In recent years, large language models (LLMs) have come into view as one of the most transformative developments in the technical domain, influencing diverse sectors ranging from natural language processing (NLP) to creative arts. Their rise signifies an unprecedented convergence of computational prowess, sophisticated algorithms, and expansive datasets, pushing the boundaries of what was once thought to be achievable. Such a profound impact mandates a thorough exploration of the LLMs’ evolutionary trajectory. Consequently, this article conducts a literature review of the most impactful scientific works, using the reliable Web of Science (WoS) indexing database as a data source in order to attain a thorough and quality-assured analysis. This review identifies relevant patterns, provides research insights, traces technological growth, and anticipates potential future directions. Beyond mapping the known, this study aims to highlight uncharted areas within the LLM landscape, thereby catalyzing future research endeavors. The ultimate goal is to enhance collective understanding, encourage collaboration, and guide subsequent innovations in harnessing the potential of LLMs for societal and technological advancement.