Papers with cross-lingual reasoning abilities
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)
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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. |
| Approach: | They propose a comprehensive benchmark covering 29 languages, built on an English benchmark. |
| Outcome: | The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark. |
Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual language models (MultiLMs) are capable of logically reasoning over natural language statements, reasoning with their implicit knowledge, and performing multi-step reasoning when the model size is large enough. |
| Approach: | They propose a mechanism that encourages cross-lingual attention in code-switched sequences and improves reasoning performance by up to 14%. |
| Outcome: | The proposed approach improves reasoning performance by 14% and 4% on the RuleTaker and LeapOfThought datasets. |