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Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning
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Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey Corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-agnostic probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method -- multilingual contrastive pre-training (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks.
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Cited by 1 Pith paper
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Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks
A survey that categorizes multilingual prompting techniques by NLP task and language family, and designates potential state-of-the-art prompting methods for each dataset.
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