Few-Shot Multilingual Open-Domain QA from 5 Examples
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910847622709248 |
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| author | Jiang, Fan Drummond, Tom Cohn, Trevor |
| author_facet | Jiang, Fan Drummond, Tom Cohn, Trevor |
| contents | Recent approaches to multilingual open-domain question answering (MLODQA) have achieved promising results given abundant language-specific training data. However, the considerable annotation cost limits the application of these methods for underrepresented languages. We introduce a \emph{few-shot learning} approach to synthesise large-scale multilingual data from large language models (LLMs). Our method begins with large-scale self-supervised pre-training using WikiData, followed by training on high-quality synthetic multilingual data generated by prompting LLMs with few-shot supervision. The final model, \textsc{FsModQA}, significantly outperforms existing few-shot and supervised baselines in MLODQA and cross-lingual and monolingual retrieval. We further show our method can be extended for effective zero-shot adaptation to new languages through a \emph{cross-lingual prompting} strategy with only English-supervised data, making it a general and applicable solution for MLODQA tasks without costly large-scale annotation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Few-Shot Multilingual Open-Domain QA from 5 Examples Jiang, Fan Drummond, Tom Cohn, Trevor Computation and Language Information Retrieval Recent approaches to multilingual open-domain question answering (MLODQA) have achieved promising results given abundant language-specific training data. However, the considerable annotation cost limits the application of these methods for underrepresented languages. We introduce a \emph{few-shot learning} approach to synthesise large-scale multilingual data from large language models (LLMs). Our method begins with large-scale self-supervised pre-training using WikiData, followed by training on high-quality synthetic multilingual data generated by prompting LLMs with few-shot supervision. The final model, \textsc{FsModQA}, significantly outperforms existing few-shot and supervised baselines in MLODQA and cross-lingual and monolingual retrieval. We further show our method can be extended for effective zero-shot adaptation to new languages through a \emph{cross-lingual prompting} strategy with only English-supervised data, making it a general and applicable solution for MLODQA tasks without costly large-scale annotation. |
| title | Few-Shot Multilingual Open-Domain QA from 5 Examples |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2502.19722 |