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

Created by
  • Haebom

Author

Ruian Ke, Ruy M. Ribeiro

Outline

This paper presents a roadmap for integrating large-scale language models (LLMs) into interdisciplinary research. Despite concerns about the hallucinations, biases, and potential harms of LLMs, we highlight that LLMs are powerful AI tools that can transform research processes, and argue that it is important to clearly understand the strengths and weaknesses of LLMs to ensure their effective and responsible use. In particular, we present ways in which LLMs can be used in interdisciplinary research, where effective communication, knowledge transfer, and collaboration across disciplines are essential, and demonstrate how repeated interactions with LLMs (ChatGPT) can facilitate interdisciplinary collaboration and research, using a case study of computational biology modeling HIV relapse dynamics. We argue that LLMs are most effective when used as adjunctive tools within a human-centered framework, and envision that responsible use of LLMs will enhance innovative interdisciplinary research and significantly accelerate scientific discovery.

Takeaways, Limitations

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Takeaways:
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Demonstrates that the LLM can facilitate effective communication, knowledge transfer and collaboration in interdisciplinary research.
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Presenting strategies for using the LLM as an adjunct tool within a human-centered framework.
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Suggesting that responsible use of the LLM in interdisciplinary research can accelerate scientific discovery.
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Demonstrating the practicality of leveraging the LLM through real-world case studies in computational biology.
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Limitations:
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The presented roadmap may have limited generalizability as it is based on case studies confined to the field of computational biology.
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LLM's solutions to problems such as hallucinations and biases are lacking.
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Lack of in-depth discussion of ethical use and accountability issues in LLMs.
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There is a lack of generalized methodological presentations for the application of LLM to various academic fields.
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