aiqbench analysis
This research investigates Representation Misdirection (RM) in large language models, demonstrating that redirecting latent representations to specific target vectors can controllably alter model behaviors such as truthfulness, bias, and sentiment beyond simple forgetting. By leveraging the Linear Representation Hypothesis, the study reveals a dual-edged mechanism where these side effects pose potential risks if misused but offer a pathway to engineer models with stronger, controllable capabilities. The findings provide critical insights into the internal mechanics of machine unlearning and its broader implications for model safety and alignment.
Beyond Forgetting: Representation Misdirection Elicits Controllable Side Behaviors and Capabilities
arXiv:2601.21702v4 Announce Type: replace-cross Abstract: We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting the latent representations of forget-samples toward a target vector. Despite being important, the roles of the target vector used in RM, however, remain underexplored. Here, we approach and revisit RM through the lens of the Linear Representation Hypothesis. Specifically, if one can identify a one-dimensional representation corresponding to a high-level concept, the Linear Representation Hypothesis enables linear operations on this concept vector within the forget-representation space. Under this view, we hypothesize that, beyond forgetting, machine unlearning via RM elicits controllable side effect behaviors and capabilities corresponding to the high-level concept. Our hypothesis is empirically validated across a wide range of concepts and tasks, including controlling unlearned models' truthfulness, sentiment, stereotypical bias, refusal, language, and in-context learning (ICL) tasks. Our findings reveal that this phenomenon could be either a hidden risk if misused or a mechanism that can be harnessed for developing unlearned models that require stronger capabilities and controllable behaviors.
Abstract:We consider Representation Misdirection (RM), a class of large language model (LLM) unlearning methods that achieve forgetting by redirecting the latent representations of forget-samples toward a target vector. Despite being important, the roles of the target vector used in RM, however, remain underexplored. Here, we approach and revisit RM through the lens of the Linear Representation Hypothesis. Specifically, if one can identify a one-dimensional representation corresponding to a high-level concept, the Linear Representation Hypothesis enables linear operations on this concept vector within the forget-representation space. Under this view, we hypothesize that, beyond forgetting, machine unlearning via RM elicits controllable side effect behaviors and capabilities corresponding to the high-level concept. Our hypothesis is empirically validated across a wide range of concepts and tasks, including controlling unlearned models' truthfulness, sentiment, stereotypical bias, refusal, language, and in-context learning (ICL) tasks. Our findings reveal that this phenomenon could be either a hidden risk if misused or a mechanism that can be harnessed for developing unlearned models that require stronger capabilities and controllable behaviors.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2601.21702 [cs.LG]
(or arXiv:2601.21702v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.21702 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 265 words
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