# PolQA: Polish Question Answering Dataset

arXiv: https://arxiv.org/abs/2212.08897 · HTML: https://mlsanity.com/paper/2212.08897/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://mlsanity.com/paper/2110.07367/) (2021)
- [Optimizing Dense Retrieval Model Training with Hard Negatives](https://mlsanity.com/paper/2104.08051/) (2021)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://mlsanity.com/paper/1711.05073/) (2017)
- [KLEJ: Comprehensive Benchmark for Polish Language Understanding](https://mlsanity.com/paper/2005.00630/) (2020)
- [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://mlsanity.com/paper/2105.01735/) (2021)
- [Evaluation of Transfer Learning for Polish with a Text-to-Text Model](https://mlsanity.com/paper/2205.08808/) (2022)
- [Boosting Question Answering by Deep Entity Recognition](https://mlsanity.com/paper/1605.08675/) (2016)

## Built on by (most cited first, 7 of 7)

- [Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation](https://mlsanity.com/paper/2410.18565/) (2024)
- [MAUPQA: Massive Automatically-created Polish Question Answering Dataset](https://mlsanity.com/paper/2305.05486/) (2023)
- [Evaluating Polish linguistic and cultural competency in large language models](https://mlsanity.com/paper/2503.00995/) (2025)
- [Bielik 11B v3: Multilingual Large Language Model for European Languages](https://mlsanity.com/paper/2601.11579/) (2025)
- [Bielik v3 Small: Technical Report](https://mlsanity.com/paper/2505.02550/) (2025)
- [Bielik 11B v2 Technical Report](https://mlsanity.com/paper/2505.02410/) (2025)
- [Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series](https://mlsanity.com/paper/2604.10799/) (2026)

## Abstract (arXiv)

Recently proposed systems for open-domain question answering (OpenQA) require large amounts of training data to achieve state-of-the-art performance. However, data annotation is known to be time-consuming and therefore expensive to acquire. As a result, the appropriate datasets are available only for a handful of languages (mainly English and Chinese). In this work, we introduce and publicly release PolQA, the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7,097,322 candidate passages. Each question is classified according to its formulation, type, as well as entity type of the answer. This resource allows us to evaluate the impact of different annotation choices on the performance of the QA system and propose an efficient annotation strategy that increases the passage retrieval accuracy@10 by 10.55 p.p. while reducing the annotation cost by 82%.