Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science

TL;DR

This paper systematically examines how Large Language Models influence research across various academic fields beyond NLP, revealing trends, disparities, and usage patterns in non-CS disciplines since 2018.

Contribution

It provides the first comprehensive empirical analysis of LLM influence in non-NLP fields, highlighting usage disparities and predominant task-agnostic applications across disciplines.

Findings

LLMs are increasingly cited in non-CS research since 2018.

Linguistics and Engineering account for about 45% of LLM citations.

Most fields use task-agnostic LLMs for domain-specific problems.

Abstract

Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP), reshaping research and extending NLP's influence to other fields of study. However, there is little to no work examining the degree to which LLMs influence other research fields. This work empirically and systematically examines the influence and use of LLMs in fields beyond NLP. We curate $106$ LLMs and analyze $\sim$$148k$ papers citing LLMs to quantify their influence and reveal trends in their usage patterns. Our analysis reveals not only the increasing prevalence of LLMs in non-CS fields but also the disparities in their usage, with some fields utilizing them more frequently than others since 2018, notably Linguistics and Engineering together accounting for $\sim$$45\%$ of LLM citations. Our findings further indicate that most of these fields predominantly employ task-agnostic LLMs, proficient in zero or few-shot learning without requiring further fine-tuning, to address their domain-specific problems. This study sheds light on the cross-disciplinary impact of NLP through LLMs, providing a better understanding of the opportunities and challenges.