Multilingual Embedding Probes Fail to Generalize Across Learner Corpora
Do multilingual embedding models encode a language-general representation of proficiency? We investigate this by training linear and non-linear probes on hidden-state activations from seven embedding models (0.3-8B) to predict CEFR proficiency levels from learner texts across nine corpora and seven languages. We compare five probing architectures against a baseline trained on surface-level text features. Under in-distribution evaluation, probes achieve strong performance (Quadratic Weighted Kappa $\approx0.7$), substantially outperforming the surface baseline, with middle layers consistently yielding the best predictions. However, in cross-corpus evaluation performance collapses across al...
| Authors | Laurits Lyngbaek, Ross Deans Kristensen-McLachlan |
|---|---|
| Primary category | cs.CL |
| Published | 8 April 2026 |
| Last updated at source | 11 August 2026 |
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