Challenge: Prior studies suggested that English instructions are more effective for non-English tasks . however, these studies often use datasets and instructions translated from English .
Approach: They conduct a fair comparison between English and target-language instructions by eliminating translationese effects.
Outcome: The results show that the advantage of adopting English instructions is not overwhelming . the results also show that instruction-following abilities are improved when using respective instructions.

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Challenge: Current LLMs are primarily trained on English data but also include data from other languages.
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Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)

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Challenge: Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora.
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Challenge: Large language models (LLMs) have a growing number of applications that generate harmful, biased, or unsafe content.
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Challenge: Current Large Language Models (LLMs) are predominantly designed with English as the primary language, but many are still English-dominated.
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Challenge: Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences.
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Challenge: EXECUTE is an expandable X(Cross)-Lingual Extension of CUTE that can be expanded to any language.
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Challenge: a study of multilingual pre-trained LLMs on parallel instruction-tuning benchmarks shows that instruction-following models can be used across languages by up to 9.9%.
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How Does Quantization Affect Multilingual LLMs? (2024.findings-emnlp)

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Challenge: Quantization is widely used to improve inference speed and deployment of large language models.
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