Translating LIBERO to ten languages shows VLA failures under multilingual instructions are driven by language-sensitive steps; a step-wise inference intervention improves performance.
Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models
4 Pith papers cite this work. Polarity classification is still indexing.
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Using a 1PL IRT model on real cultural questions across 13 locales, the study identifies a local-language knowledge-access advantage masked by lower proficiency in raw accuracy.
Luar is a reinforcement learning method enabling reasoning language models to decide when to invoke English translation for improved multilingual reasoning.
Unsupervised RL enforces cross-lingual self-consistency to improve multilingual math reasoning by up to 21.7% on MGSM without gold answers or parallel data, with generalization to unseen languages.
citing papers explorer
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When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models
Translating LIBERO to ten languages shows VLA failures under multilingual instructions are driven by language-sensitive steps; a step-wise inference intervention improves performance.
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The Masked Advantage: Uncovering Local-Language Access to Cultural Knowledge in LLMs
Using a 1PL IRT model on real cultural questions across 13 locales, the study identifies a local-language knowledge-access advantage masked by lower proficiency in raw accuracy.
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Learning When to Translate for Multilingual Reasoning
Luar is a reinforcement learning method enabling reasoning language models to decide when to invoke English translation for improved multilingual reasoning.
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Cross-lingual Self-Consistency for Multilingual Reasoning with Language Models
Unsupervised RL enforces cross-lingual self-consistency to improve multilingual math reasoning by up to 21.7% on MGSM without gold answers or parallel data, with generalization to unseen languages.