Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners (2024.emnlp-main)
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Shimao Zhang, Changjiang Gao, Wenhao Zhu, Jiajun Chen, Xin Huang, Xue Han, Junlan Feng, Chao Deng, Shujian Huang
| Challenge: | Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages. |
| Approach: | They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods. |
| Outcome: | The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs. |
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| Challenge: | Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities, but performance and cross-lingual alignment often lag for non-dominant languages. |
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| Challenge: | Experiments show that models trained on multi-way parallel data outperform those trained on unaligned data. |
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| Challenge: | Large language models (LLMs) have impressive translation capabilities even without being explicitly trained on parallel data. |
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Alexander Weber, Klaudia Thellmann, Jan Ebert, Nicolas Flores-Herr, Jens Lehmann, Michael Fromm, Mehdi Ali
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| Challenge: | Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data. |
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Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. |
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Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, JingBo Zhu
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Concept Space Alignment in Multilingual LLMs (2024.emnlp-main)
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| Challenge: | Multilingual large language models generalize somewhat across languages, but it is unclear whether this is a result of improved, implicit alignment, or of something else, e.g., linguistic overlap or semi-parallel subsets of training data. |
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Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? (2024.findings-emnlp)
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| Challenge: | Existing models that target a single language are not seen during finetuning, but are able to respond in multiple languages once deployed in downstream applications. |
| Approach: | They investigate the minimal amount of multilinguality required during finetuning to elicit effective cross-lingual generalisation in English-centric LLMs. |
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