Cross-lingual probe transfer and direct comparison of probe weights show that NLI representations are most alignable in the middle layers: probes transfer best there, and probes trained independently on different languages converge to similar weight vectors, peaking mid-network.
As Large Language Models (LLMs) grow more capable across diverse tasks, their (in)ability to generalize remains difficult to quantify and poorly understood beyond limited domains. In particular, LLMs are known to struggle generalizing multilingually, to languages outside of English, and that are poorly attested in thei...
Supantho Rakshit, Adele E. Goldberg, Henry Conklin· arXiv.org· 0 citations
A PMI-based translation metric is proposed, which is less dependent on the target language and correlates strongly with chrF, and finds that CLA with English predicts translation quality comparably to or better than source-target CLA.
Adnan Al Ali, Kathy Hämmerl, Jindrich Libovický et al.· 0 citations
It is suggested that cross-lingual inconsistency is at least partly a selection problem, and that simple contextual interventions may outperform more invasive methods for robust, transferable alignment.
This work presents the first systematic study of inverse relation directionality in LLMs, using a benchmark consisting of 5,457 instances spanning 27 distinct inverse relation labels and reveals systematic asymmetries in inverse relation classification across LLMs.
This work demonstrates that cross-lingual word-to-speech mappings can be learned directly from visual grounding without transcriptions or explicit model training.
Gabriel Pirlogeanu, Dan Oneata, H. Cucu et al.· 0 citations
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