Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS
This paper introduces LENS, a protocol to evaluate whether machine unlearning can suppress disinformation-aligned narrative frames in LLMs. Experiments on four multilingual models show effective suppression but also reveal entity recovery side effects.
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[Submitted on 27 Jun 2026]
Title:Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS
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Abstract:Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior. We introduce Level-based Evaluation of Narrative Suppression (LENS), a contextualization based evaluation protocol for testing target narrative reproduction across direct, attributed, contrastive, and abstract resistance levels. We evaluate two source-grounded narratives: one framing Russia's war against Ukraine as forced by NATO expansion, and one framing the United States as exploiting or abandoning Taiwan. The experiments cover four near-12B multilingual instruction models: Lapa LLM, Gemma-12B, Qwen-14B, and TAIDE-Gemma.
We introduce the Suppression-Collapse Efficiency (SCE) score as a checkpoint selection summary that rewards target-narrative suppression while penalizing degraded outputs. Our results shows that selected checkpoints can reduce narrative reproduction and suppression may transfer beyond direct forget prompts. We also report entity recovery as a separate side effect: abstract A/B/C prompts can cause models to recover the real-world actors associated with the target frame after unlearning. These findings demonstrate that LENS is a successful diagnostic protocol for both reporting and guiding the further study of the deeper structure of narrative unlearning.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22657 [cs.CL]
(or arXiv:2607.22657v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.22657
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Viktoriia Makovska [view email] [v1] Sat, 27 Jun 2026 15:20:38 UTC (158 KB)
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