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 provide comprehensive guidelines for deploying LLM guardrails in the scientific domain. We identify specific challenges -- including time sensitivity, knowledge contextualization, conflict resolution, and intellectual property concerns -- and propose a guideline framework for the guardrails that can align with scientific needs. These guardrail dimensions include trustworthiness, ethics & bias, safety, and legal aspects. We also outline in detail the implementation strategies that employ white-box, black-box, and gray-box methodologies that can be enforced within scientific contexts.

翻译：大型语言模型（LLMs）的快速发展已经改变了自然语言处理与理解（NLP/NLU）的格局，为各个领域带来了显著益处。然而，当这些强大模型应用于科学研究时，它们在科学诚信与可信度方面表现出关键性的失效模式。现有的通用LLM护栏不足以应对科学领域的这些独特挑战。我们为在科学领域部署LLM护栏提供了全面的指导方针。我们识别了具体挑战——包括时间敏感性、知识情境化、冲突解决和知识产权问题——并提出了一个能与科学需求相匹配的护栏指导框架。这些护栏维度包括可信度、伦理与偏见、安全性以及法律层面。我们还详细概述了可在科学背景下实施的实现策略，这些策略采用了白盒、黑盒和灰盒方法。