Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology

TL;DR

This paper presents a roadmap for integrating large language models into cross-disciplinary research, exemplified by computational biology, highlighting their potential to enhance collaboration and accelerate scientific discovery.

Contribution

It offers a detailed framework for responsible LLM use in interdisciplinary research, including a case study on modeling HIV rebound dynamics.

Findings

LLMs can facilitate interdisciplinary collaboration.

Iterative interactions with LLMs improve research outcomes.

Responsible use enhances scientific discovery.

Abstract

Large language models (LLMs) are powerful artificial intelligence (AI) tools transforming how research is conducted. However, their use in research has been met with skepticism, due to concerns about hallucinations, biases and potential harms to research. These emphasize the importance of clearly understanding the strengths and weaknesses of LLMs to ensure their effective and responsible use. Here, we present a roadmap for integrating LLMs into cross-disciplinary research, where effective communication, knowledge transfer and collaboration across diverse fields are essential but often challenging. We examine the capabilities and limitations of LLMs and provide a detailed computational biology case study (on modeling HIV rebound dynamics) demonstrating how iterative interactions with an LLM (ChatGPT) can facilitate interdisciplinary collaboration and research. We argue that LLMs are best used as augmentative tools within a human-in-the-loop framework. Looking forward, we envisage that the responsible use of LLMs will enhance innovative cross-disciplinary research and substantially accelerate scientific discoveries.