← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-srw.3 · 记录 #1
Language Directions in Multilingual LLMs: A Layer-wise Diagnostic Study of Token Alignment and Pretraining Imprint
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
We investigate how multilingual representations emerge across depth in large language models.Using a unified probing framework, we analyze six multilingual LLMs across five languages (EN/ES/ZH/FR/DE), decomposing behavior into (i) early-layer dynamics, (ii) linear vs. MLP separability, and (iii) token–language alignment that tracks where vocabulary sharing peaks.Across models, we observe a consistent and substantial early jump: accuracy rises by +73.5 to +80.7 points from L0 to L1 on average, indicating that language-relevant signals become accessible immediately after the embedding layer.Moreover, representations are largely linearly separable: for 5/6 models, the mean gap between MLP and linear probes remains within \pm0.5 points.Token–language alignment further reveals systematic structure, with peak vocabulary mass exceeding 48% in some models and substantial variation in the depth of peak sharing.These findings provide a compact, cross-model characterization of how multilingual information is organized across depth and introduce simple alignment metrics that complement accuracy-based evaluation.
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记录 #1 · 2026-07 · 最新观测版本
Language Directions in Multilingual LLMs: A Layer-wise Diagnostic Study of Token Alignment and Pretraining Imprint
We investigate how multilingual representations emerge across depth in large language models.Using a unified probing framework, we analyze six multilingual LLMs across five languages (EN/ES/ZH/FR/DE), decomposing behavior into (i) early-layer dynamics, (ii) linear vs. MLP separability, and (iii) token–language alignment that tracks where vocabulary sharing peaks.Across models, we observe a consistent and substantial early jump: accuracy rises by +73.5 to +80.7 points from L0 to L1 on average, indicating that language-relevant signals become accessible immediately after the embedding layer.Moreover, representations are largely linearly separable: for 5/6 models, the mean gap between MLP and linear probes remains within \pm0.5 points.Token–language alignment further reveals systematic structure, with peak vocabulary mass exceeding 48% in some models and substantial variation in the depth of peak sharing.These findings provide a compact, cross-model characterization of how multilingual information is organized across depth and introduce simple alignment metrics that complement accuracy-based evaluation.
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