A practical risk framework for large language model use in life science research

The useful part

We explain how LLM architecture produces both remarkable capabilities and characteristic failures including hallucination and sycophancy, with particular attention to vulnerabilities most relevant to life science workflows. Together, these resources are designed to help researchers use these tools carefully and document how they did so. Davis II, Alexandra Alexiev Responsible use of large language models (LLMs) in life science research demands a unified approach to risk, yet existing guidance treats prompting and verification as separate topics rather than as integrated components of a single risk management framework.

How it works

• Davis II, Alexandra Alexiev Responsible use of large language models (LLMs) in life science research demands a unified approach to risk, yet existing guidance treats prompting and verification as separate...
• This paper addresses that gap for life science researchers.
• We apply both layers to common research tasks, including literature synthesis, code generation, writing assistance, statistical reasoning, and administrative work, with documentation practices and ethical...
• Six supplementary guides extend each component into detailed workflows, worked examples, and reference checklists, and a companion open-source repository (https://github.com/SharptonLab/PromptLab) provides...