# Transforming scholarly landscapes: The influence of large language models on academic fields beyond computer science

## TL;DR

This paper explores how large language models are influencing academic fields outside of computer science, revealing trends and disparities in their usage.

## Contribution

The study provides the first empirical analysis of LLM influence across non-CS fields using a curated dataset of 106 LLMs and 148k papers.

## Key findings

- LLMs are increasingly used in non-CS fields, with Linguistics and Engineering accounting for 45% of citations.
- Task-agnostic LLMs are predominantly used for domain-specific problems without fine-tuning.
- There are significant disparities in LLM usage across different academic fields since 2018.

## 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 ∼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 ∼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.