Online Language Adaptive Sampling for Better Distributed Cross-lingual Gains
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arXiv:2604.13286v2 Announce Type: replace Abstract: Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing...
arXiv:2604. 08564v2 Announce Type: replace-cross Abstract: Auto-regressive models (ARMs) have established a dominant paradigm in language modeling.
arXiv:2510. 15551v2 Announce Type: replace-cross Abstract: Any piece of knowledge is usually expressed in one or a handful of natural languages on the web or in any large corpus.
arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
arXiv:2609.15758v1 Announce Type: new Abstract: Extending large-scale multilingual automatic speech recognition (ASR) models to low-resource languages remains challenging. Model performance is skewed...
The paper investigates multilingual confidence calibration in large language models, revealing that non‑English languages are systematically less well calibrated than English. By analyzing internal representations, the authors find that late‑intermediate layers provide a more reliable confidence signal than the final layer, which is biased by English‑centric training. They propose training‑free methods such as Language‑Aware Confidence Ensemble (LACE) to adaptively select optimal layers per language, aiming to improve global equity and trustworthiness of LLMs.