Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology
This work addresses the problem of skepticism and inefficiency in using LLMs for research across diverse fields, offering a practical guide for researchers, though it is incremental in applying existing methods to new contexts.
The paper tackles the challenge of integrating large language models (LLMs) into cross-disciplinary research by presenting a roadmap and a computational biology case study on modeling HIV rebound dynamics, demonstrating how iterative interactions with ChatGPT can facilitate collaboration and accelerate discoveries.
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.