The Multilingual Mind : A Survey of Multilingual Reasoning in Language Models

要約

While reasoning and multilingual capabilities in Language Models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm, multilingual reasoning, is at a nascent stage.
Multilingual reasoning requires language models to handle logical reasoning across languages while addressing misalignment, biases, and challenges in low-resource settings.
この調査では、LMSでの多言語推論の最初の詳細なレビューを提供します。
この調査では、多言語の推論にLMSを活用する既存の方法の体系的な概要を提供し、特に言語モデルを適用することの課題、動機、および基礎的側面を多様な言語を超えて適用することの基礎的側面を概説します。
LMSでの多言語推論のトレーニングに使用される標準のデータリソースと、多言語機能を評価するために採用された評価ベンチマークの概要を説明します。
次に、これらのベンチマークでのさまざまな最先端の方法とそのパフォーマンスを分析します。
最後に、LMSの多言語推論を改善するための将来の研究の機会を探り、多様な言語と複雑な推論タスクを処理する能力の向上に焦点を当てています。

要約(オリジナル)

While reasoning and multilingual capabilities in Language Models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm, multilingual reasoning, is at a nascent stage. Multilingual reasoning requires language models to handle logical reasoning across languages while addressing misalignment, biases, and challenges in low-resource settings. This survey provides the first in-depth review of multilingual reasoning in LMs. In this survey, we provide a systematic overview of existing methods that leverage LMs for multilingual reasoning, specifically outlining the challenges, motivations, and foundational aspects of applying language models to reason across diverse languages. We provide an overview of the standard data resources used for training multilingual reasoning in LMs and the evaluation benchmarks employed to assess their multilingual capabilities. Next, we analyze various state-of-the-art methods and their performance on these benchmarks. Finally, we explore future research opportunities to improve multilingual reasoning in LMs, focusing on enhancing their ability to handle diverse languages and complex reasoning tasks.

arxiv情報

提供元, 利用サービス

arxiv.jp, Google