arXiv cs.CL· Giang Son Nguyen, Wray Buntine, Dung D. Le, Wenya Wang·· 12 小时前AI 评分34
多语言 LLM 中的否定表征：一项因果分析
"Not" is Not Lost in Translation: A Causal Analysis of Negation Representations in Multilingual LLMs
AI 导读
研究者在六个开源模型上对英语、法语、越南语和中文做因果干预，发现多语言 LLM 的否定表征可被因果控制且跨语言高度共享：从一种语言提取的对比激活方向能在其他语言中诱导或移除否定，不同语言提取的方向也基本对齐。
正文 · 原文
Abstract:Negation occurs in every language, yet its surface realization differs drastically. Despite this, multilingual large language models (LLMs) translate negation robustly, raising the question of whether they share an underlying representation of negation across languages. We study this question in six open models across English, French, Vietnamese, and Chinese, using causal interventions on a synthetic contrastive dataset and a negation translation dataset derived from FLORES-200. Across models, we find strong evidence that negation representations are causally controllable and largely shared across languages. In particular, we show that a contrastive activation direction extracted from one language can induce or remove negation in the others, and directions extracted from different languages are substantially aligned. We further find that negation is represented in sparse features: a single sparse autoencoder (SAE) feature per model suffices to control polarity across languages, while responding selectively to negated inputs. Finally, both dense and sparse interventions generalize to four additional languages and FLORES-derived sentences. We release data and code to support future work: this https URL.
| Comments: | 29 pages, 20 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32281 [cs.CL] |
| (or arXiv:2610.32281v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32281 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Giang Son Nguyen [view email]
[v1]
Fri, 23 Oct 2026 07:38:41 UTC (1,034 KB)
来源：arXiv cs.CL · arxiv.org