Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages (2023.findings-acl)
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| Challenge: | Multilingual pretrained language models (MPLMs) perform strongly in cross-lingual transfer. |
| Approach: | They propose to augment context with similar sentences retrieved from a high-resource language (HRL) they find a significant correlation between cross-lingual transfer performance and similarity between high- and low-resourced languages . |
| Outcome: | The proposed model outperforms finetuning by 3.7% on three downstream tasks with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled and labeled settings. |
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| Challenge: | NeighXLM is a neighbor-augmented contrastive pretraining framework . it exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs. |
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Enhancing Cross-lingual Prompting with Dual Prompt Augmentation (2023.findings-acl)
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| Challenge: | In-context learning (ICL) is a widely adopted technique for learning large language models . however, there is little systematic understanding of when and why it works well . |
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
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| Challenge: | Existing work focuses on monolingual prompts, but we study multilingual prompt for multilingual models. |
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| Challenge: | Existing approaches to extend semantic parsing (SP) beyond English are challenging due to the complex slot alignment step after translation. |
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