Multilingual LLMs share unified negation representations across languages
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers have identified a shared causal substrate for negation across languages in multilingual LLMs, demonstrating that activation patterns extracted from one language can reliably control negation in others. Testing six open models on English, French, Vietnamese, and Chinese reveals that despite surface-level syntactic differences, these systems encode negation through a unified internal mechanism. This finding has implications for mechanistic interpretability work and suggests multilingual models develop language-agnostic semantic primitives, potentially informing both model design and our understanding of how neural networks compress cross-linguistic structure.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The paper doesn't just show that negation works across languages, but that you can extract activation patterns from one language and use them to control negation in another. That's a step beyond documenting shared structure: it's demonstrating causal control, which is mechanistically stronger than correlation.
This connects directly to our October 20th coverage of polarity blindness in LLM safety, which found that models systematically fail to recognize harmful intent when requests are negated. That work showed negation as a failure point; this new study reveals the internal mechanism that might explain why. The finding also builds on our October 22nd piece on metaphor steering, which demonstrated that you can locate and manipulate specific semantic subspaces. Here, negation appears to be one such universal subspace across languages. The implication is that if negation has a unified causal substrate, the safety vulnerabilities documented in polarity blindness may stem from a single architectural bottleneck rather than language-specific gaps.
Test whether adversarial negation attacks (the kind that bypassed safety in the October 20th study) can be mitigated by directly intervening on the negation subspace identified here. If steering the shared negation substrate blocks polarity-based jailbreaks across multiple languages simultaneously, that confirms the causal link and opens a concrete defense path.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
Leading LLMs fail to detect harmful intent through negation and antonym tricks
Researchers have identified a structural vulnerability in leading language models where safety guardrails fail after linguistic transformations that invert meaning. Testing across 10 state-of-the-art systems in three languages, the study reveals that models struggle to recognize harmful intent when requests are negated, rephrased with antonyms, or wrapped in adversarial framing. This polarity blindness represents a…
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “"Not" is Not Lost in Translation: A Causal Analysis of Negation Representations in Multilingual LLMs”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.