测试大模型能否删掉过时科学观点，发现现有方法效果差。
Can Scientific Claims Be Removed from Large Language Models? A Systematic Evaluation of Claim-Level Unlearning
- 提出科学观点级遗忘任务与新基准SciUnlearn
- 现有方法仅能表面抑制，无法真正删除过时知识
- 适合关注AI可信度与知识更新的研究者
语言模型在静态科学语料上训练，但科学知识持续演进，旧观点可能被推翻或修正，导致模型传播过时信息。为应对这一问题，需让模型具备遗忘过时科学观点的能力。机器遗忘提供了一种解决方案，可在保持模型整体性能的同时移除特定知识。然而，现有研究多聚焦实例级遗忘，而科学观点具有强关联性且动态演化，带来额外挑战。为此，本文提出科学观点级遗忘任务，并构建新基准SciUnlearn。实验表明，当前遗忘方法难以有效清除观点级知识，常仅实现表面抑制，凸显了针对结构化知识设计专用遗忘方法的必要性。
原文摘要 · Abstract (English)
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models may later become retracted, disproven, or updated by subsequent research, creating the risk of disseminating outdated information in scientific workflows.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.
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