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

Multilingual proficiency presents a significant challenge for large language
models (LLMs). English-centric models are usually suboptimal in other
languages, particularly those that are linguistically distant from English.
This performance discrepancy mainly stems from the imbalanced distribution of
training data across languages during pre-training and instruction tuning
stages. To address this problem, we propose a novel approach called CrossIn,
which utilizes a mixed composition of cross-lingual instruction tuning data.
Our method leverages the compressed representation shared by various languages
to efficiently enhance the model's task-solving capabilities and multilingual
proficiency within a single process. In addition, we introduce a multi-task and
multi-faceted benchmark to evaluate the effectiveness of CrossIn. Experimental
results demonstrate that our method substantially improves performance across
tasks and languages, and we provide extensive insights into the impact of
cross-lingual data volume and the integration of translation data on enhancing
multilingual consistency and accuracy.

通过使用 CrossIn 方法，利用跨语言指导调整数据的混合构成，我们有效地提升了大语言模型在多语种任务和多语言能力上的表现，并对跨语言数据量和翻译数据的整合对提高多语言一致性和准确性的影响进行了广泛探讨。