A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities

TL;DR

This survey reviews how Large Language Models are applied across various disciplines, highlighting methodologies, challenges, and future opportunities to enhance interdisciplinary research.

Contribution

It provides a comprehensive categorization of technical methods and applications of LLMs in discipline-specific research, addressing current challenges and future directions.

Findings

LLMs are increasingly used in diverse disciplines like mathematics and social sciences.

Key methodologies include fine-tuning, retrieval-augmented generation, and tool integration.

Challenges include domain adaptation and interpretability.

Abstract

Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering interdisciplinary collaboration. However, a systematic understanding of their integration into diverse disciplines remains underexplored. This survey paper provides a comprehensive overview of the application of LLMs in interdisciplinary studies, categorising research efforts from both a technical perspective and with regard to their applicability. From a technical standpoint, key methodologies such as supervised fine-tuning, retrieval-augmented generation, agent-based approaches, and tool-use integration are examined, which enhance the adaptability and effectiveness of LLMs in discipline-specific contexts. From the perspective of their applicability, this paper explores how LLMs are contributing to various disciplines including mathematics, physics, chemistry, biology, and the humanities and social sciences, demonstrating their role in discipline-specific tasks. The prevailing challenges are critically examined and the promising research directions are highlighted alongside the recent advances in LLMs. By providing a comprehensive overview of the technical developments and applications in this field, this survey aims to serve as an invaluable resource for the researchers who are navigating the complex landscape of LLMs in the context of interdisciplinary studies.