CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs). While mPTMs show the potential to encode commonsense knowledge for different languages, transferring commonsense knowledge learned in large-scale English corpus to other languages is challenging. To address this problem, we propose the attention-based Cross-LIngual Commonsense Knowledge transfER (CLICKER) framework, which minimizes the performance gaps between English and non-English languages in commonsense question-answering tasks. CLICKER effectively improves commonsense reasoning for non-English languages by differentiating non-commonsense knowledge from commonsense knowledge. Experimental results on public benchmarks demonstrate that CLICKER achieves remarkable improvements in the cross-lingual CSR task for languages other than English.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 12 of its 15 references that are in the map. First posted on arXiv 26 Feb 2023.
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References · 12
- 2017Attention Is All You Needcited by 59,932
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- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
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- 2019Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approachescited by 64
- 2021Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoningcited by 64
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- 2025MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning
- 2023Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention
- 2024mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans
- 2023Commonsense Knowledge Transfer for Pre-trained Language Models
- 2026XHotpotQA: A Benchmark for Cross-Lingual Knowledge Composition in Multi-Hop Question Answering
- 2025When Language Shapes Thought: Cross-Lingual Transfer of Factual Knowledge in Question Answering
- 2025CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering
- 2025Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures