Few-Shot Multilingual Open-Domain QA from Five Examples
Year: 2025
Type: article
Abstract: 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 synthesize 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, 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 cross-lingual prompting strategy with only English-supervised data, making it a general and applicable solution for MLODQA tasks without costly large-scale annotation.
DOI: 10.1162/tacl_a_00750
Authors: Fan Jiang, Tom Drummond, Trevor Cohn
Institution: The University of Melbourne
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411204721