Papers with cross-lingual Chain-of-Thought

2 papers
CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation (2025.findings-emnlp)

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Challenge: Multilingual Large Language Models (MLLMs) exhibit strong generalization across languages, yet they remain prone to hallucinations due to training data imbalances.
Approach: They propose a cross-lingual Chain-of-Thought framework that enhances cross-linguistic alignment . the framework guides the model to reason in a high-resource language before generating answers in low-resourced language.
Outcome: The proposed framework reduces hallucination rates by up to 62% and significantly improves factual knowledge transfer across language pairs.
A Reinforcement Learning Framework for Cross-Lingual Stance Detection Using Chain-of-Thought Alignment (2025.findings-acl)

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Challenge: Existing approaches to cross-lingual stance detection can't effectively perform cross-linguistic transfer of complex reasoning processes.
Approach: They propose a framework to facilitate cross-lingual transfer of complex reasoning processes in stance detection by using cross-linguistic Chain-of-Thought alignment to obtain high-quality CoTs generated from target language inputs.
Outcome: The proposed framework outperforms competing models on four multilingual datasets.

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