为科学领域大模型设计专用防护机制，解决可信度与合规难题
Challenges in Guardrailing Large Language Models for Science
- 针对科学场景提出信任、伦理、安全、法律四维防护框架
- 识别时间敏感性、知识上下文等四大独特挑战
- 支持白盒/黑盒/灰盒多种实现方式，适配科研严谨性需求
大语言模型的快速发展重塑了自然语言处理与理解领域，但在科学应用中暴露出影响科学诚信与可信度的关键缺陷。现有通用型防护机制无法应对科学领域的特殊挑战。本文提出面向科学领域的完整防护指南，识别出时间敏感性、知识上下文、冲突解决及知识产权等核心问题，并构建涵盖可信度、伦理与偏见、安全性、法律合规的四维防护框架。详细阐述了可在科研场景中实施的白盒、黑盒与灰盒策略，确保模型输出符合科学研究的严谨要求。
原文摘要 · Abstract (English)
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.
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