mlsanityData to 14 Oct 2026

Few-Shot Multilingual Open-Domain QA from 5 Examples

arXiv 2502.19722 ↗First posted 27 Feb 2025cs.CL cs.IRcited by 0

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Abstract (arXiv)

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 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, textscFsModQA, 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 cross-lingual prompting strategy with only English-supervised data, making it a general and applicable solution for MLODQA tasks without costly large-scale annotation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 27 Feb 2025.

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References · 27

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
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  12. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 407
15 more
  1. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  2. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 235
  3. 2019WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipediacited by 223
  4. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 165
  5. 2020MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answeringcited by 153
  6. 2021Simple Entity-Centric Questions Challenge Dense Retrieverscited by 140
  7. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
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  9. 2021One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrievalcited by 46
  10. 2022Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformercited by 45
  11. 2022QAmeleon: Multilingual QA with Only 5 Examplescited by 25
  12. 2023Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrievalcited by 24
  13. 2021You Only Need One Model for Open-domain Question Answeringcited by 17
  14. 2023Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrievalcited by 5
  15. 2024Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervisioncited by 3

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