Large language models (LLMs) are at the forefront of transforming numerous
domains globally. However, their inclusivity and effectiveness remain limited
for non-Latin scripts and low-resource languages. This paper tackles the
imperative challenge of enhancing the multilingual performance of LLMs without
extensive training or fine-tuning. Through systematic investigation and
evaluation of diverse languages using popular question-answering (QA) datasets,
we present novel techniques that unlock the true potential of LLMs in a
polyglot landscape. Our approach encompasses three key strategies that yield
significant improvements in multilingual proficiency. First, by meticulously
optimizing prompts tailored for polyglot LLMs, we unlock their latent
capabilities, resulting in substantial performance boosts across languages.
Second, we introduce a new hybrid approach that synergizes LLM Retrieval
Augmented Generation (RAG) with multilingual embeddings and achieves improved
multilingual task performance. Finally, we introduce a novel learning approach
that dynamically selects the optimal prompt strategy, LLM model, and embedding
model per query at run-time. This dynamic adaptation maximizes the efficacy of
LLMs across languages, outperforming best static and random strategies.
Additionally, our approach adapts configurations in both offline and online
settings, and can seamlessly adapt to new languages and datasets, leading to
substantial advancements in multilingual understanding and generation across
diverse languages.

通过优化 LLM 的提示策略、使用混合方法融合 LLM 检索增强生成和多语言嵌入，以及动态选择最佳提示策略、LLM 模型和嵌入模型，我们提出了一种能够显著提高多语言能力的新技术。

弥补鸿沟：提升 LLM 多语言表现的动态学习策略

Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual  Performance in LLMs

Large language models (LLMs) are at the forefront of transforming numerous
domains globally. However, their inclusivity and effectiveness remain limited
for non-Latin scripts and low-resource languages. This paper tackles the
imperative challenge of enhancing the multilingual performance of LLMs,
specifically focusing on Generative models. Through systematic investigation
and evaluation of diverse languages using popular question-answering (QA)
datasets, we present novel techniques that unlock the true potential of LLMs in
a polyglot landscape. Our approach encompasses three key strategies that yield
remarkable improvements in multilingual proficiency. First, by meticulously
optimizing prompts tailored for polyglot LLMs, we unlock their latent
capabilities, resulting in substantial performance boosts across languages.
Second, we introduce a new hybrid approach that synergizes GPT generation with
multilingual embeddings and achieves significant multilingual performance
improvement on critical tasks like QA and retrieval. Finally, to further propel
the performance of polyglot LLMs, we introduce a novel learning algorithm that
dynamically selects the optimal prompt strategy, LLM model, and embeddings per
query. This dynamic adaptation maximizes the efficacy of LLMs across languages,
outperforming best static and random strategies. Our results show substantial
advancements in multilingual understanding and generation across a diverse
range of languages.

本文旨在提高多语言 LLMS 的性能，通过针对泛语言生成模型的系统调查和评估，在流利问答数据集上提出了新的技术，跨越了多种语言，从而提高了其多语言能力。