FoQA: A Faroese Question-Answering Dataset
This addresses a resource gap for Faroese NLP researchers, but it is incremental as it applies existing methods to a new language.
The authors tackled the lack of resources for Faroese language processing by creating FoQA, a Faroese question-answering dataset with 2,000 validated samples, and provided baseline performance metrics across models like LLMs and BERT.
We present FoQA, a Faroese extractive question-answering (QA) dataset with 2,000 samples, created using a semi-automated approach combining Large Language Models (LLMs) and human validation. The dataset was generated from Faroese Wikipedia articles using GPT-4-turbo for initial QA generation, followed by question rephrasing to increase complexity and native speaker validation to ensure quality. We provide baseline performance metrics for FoQA across multiple models, including LLMs and BERT, demonstrating its effectiveness in evaluating Faroese QA performance. The dataset is released in three versions: a validated set of 2,000 samples, a complete set of all 10,001 generated samples, and a set of 2,395 rejected samples for error analysis.