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

Area of Science:

• Artificial Intelligence in Scientific Research
• Computational Biology
• Interdisciplinary Studies

Background:

• Large language models (LLMs) offer transformative potential for research but face skepticism due to concerns like hallucinations and biases.
• Effective communication and collaboration are crucial but challenging in cross-disciplinary research.

Purpose of the Study:

• To present a roadmap for integrating LLMs into cross-disciplinary research.
• To examine the capabilities and limitations of LLMs in a scientific context.
• To demonstrate LLM utility through a computational biology case study.

Main Methods:

• Literature review of LLM capabilities and limitations in research.
• Development of a roadmap for LLM integration in interdisciplinary settings.
• A computational biology case study modeling HIV rebound dynamics using ChatGPT.

Main Results:

• Iterative interactions with LLMs like ChatGPT can enhance interdisciplinary collaboration and research processes.
• LLMs demonstrate potential in facilitating knowledge transfer and communication across diverse scientific fields.
• The case study successfully illustrated the application of LLMs in complex biological modeling.

Conclusions:

• LLMs should be utilized as augmentative tools within a human-in-the-loop framework for responsible research.
• The strategic integration of LLMs can overcome communication barriers in cross-disciplinary research.
• Responsible LLM use is poised to accelerate scientific innovation and discovery.