Can Scientific Claims Be Removed from Large Language Models? A Systematic Evaluation of Claim-Level Unlearning

2026-08-21

AI Summary

研究探讨了如何从大型语言模型中移除过时的科学声明，通过引入科学声明遗忘任务及新基准SciUnlearn，指出需要专门方法来有效删除结构化知识。

Abstract

Language models (LMs) are trained on static scientific corpora, whereas scientific knowledge continuously evolves through correction and revision. Scientific claims encoded within these models may later become retracted, disproven, or updated by subsequent research, creating the risk of disseminating outdated information in scientific workflows. This creates a need for LMs to forget obsolete scientific claims. Machine unlearning offers a promising solution by enabling knowledge removal while maintaining overall model utility. Existing studies primarily investigate instance-level forgetting; however, scientific claims introduce additional challenges because they are interconnected, and continually evolving. To address this gap, we introduce the task of Scientific Claim Unlearning and present a new benchmark, SciUnlearn. We show that current unlearning approaches are unable to effectively eliminate claim-level knowledge and often achieve only superficial suppression, highlighting the need for specialized methods designed for structured knowledge removal.

Problem

• Scientific Claims
• Language Models
• Unlearning
• Outdated Information

Innovation

• Scientific Claim Unlearning
• SciUnlearn
• claim-level unlearning