The rapid development in large language models (LLMs) has transformed the landscape of natural language processing and
understanding (NLP/NLU), offering significant benefits across various domains. However, when applied to scientific research, these
powerful models exhibit critical failure modes related to scientific integrity and trustworthiness. Existing general-purpose LLM
guardrails are insufficient to address these unique challenges in the scientific domain. We propose a comprehensive taxonomic
framework for LLM guardrails encompassing four key dimensions: trustworthiness, ethics & bias, safety, and legal compliance.
Our framework includes structured implementation guidelines for scientific research applications, incorporating white-box, blackbox, and gray-box methodologies. This approach specifically addresses critical challenges in scientific LLM deployment, including temporal sensitivity, knowledge contextualization, conflict resolution, and intellectual property protection.
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Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
Latest posts by Ryan Watkins (see all)
- Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails - April 16, 2025
- AI-University: An LLM-based platform for instructional alignment to scientific classrooms - April 15, 2025
- Interaction-Required Suggestions for Control, Ownership, and Awareness in Human-AI Co-Writing - April 14, 2025