用LLM辅助跨学科研究，以艾滋病反弹建模为例。
Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology
- 以人机协同框架迭代使用ChatGPT，推动跨领域合作。
- 在艾滋病病毒反弹建模中实现高效知识整合与假设生成。
- 适合希望提升科研效率的跨学科研究者参考。
大型语言模型（LLMs）是变革研究方式的强大人工智能工具，但其应用常因幻觉、偏见及潜在危害而受到质疑。这凸显了明确理解其优劣势的重要性，以确保有效且负责任地使用。本文提出一个将LLMs融入跨学科研究的路线图，强调在多元领域间实现有效沟通、知识传递与协作的必要性。通过计算生物学案例（模拟艾滋病毒反弹动态），展示了与ChatGPT进行迭代交互如何促进跨学科合作与研究进展。我们主张，LLMs应作为人类在环中的增强工具使用。展望未来，负责任地使用LLMs将推动创新性跨学科研究，显著加速科学发现。
原文摘要 · Abstract (English)
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.
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