# MAUPQA: Massive Automatically-created Polish Question Answering Dataset

arXiv: https://arxiv.org/abs/2305.05486 · HTML: https://mlsanity.com/paper/2305.05486/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 27 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking](https://mlsanity.com/paper/2110.07367/) (2021)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset](https://mlsanity.com/paper/2108.13897/) (2021)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [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)
- [Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval](https://mlsanity.com/paper/2203.05765/) (2022)
- [Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering](https://mlsanity.com/paper/2004.11892/) (2020)
- [MFAQ: a Multilingual FAQ Dataset](https://mlsanity.com/paper/2109.12870/) (2021)
- [PolQA: Polish Question Answering Dataset](https://mlsanity.com/paper/2212.08897/) (2022)

## Built on by (most cited first, 11 of 11)

- [PL-MTEB: Polish Massive Text Embedding Benchmark](https://mlsanity.com/paper/2405.10138/) (2024)
- [BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language](https://mlsanity.com/paper/2305.19840/) (2023)
- [PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods](https://mlsanity.com/paper/2402.13350/) (2024)
- [MultiVENT: Multilingual Videos of Events with Aligned Natural Text](https://mlsanity.com/paper/2307.03153/) (2023)
- [Model-Aware Tokenizer Transfer](https://mlsanity.com/paper/2510.21954/) (2025)
- [Silver Retriever: Advancing Neural Passage Retrieval for Polish Question Answering](https://mlsanity.com/paper/2309.08469/) (2023)
- [Assessing generalization capability of text ranking models in Polish](https://mlsanity.com/paper/2402.14318/) (2024)
- [MessIRve: A Large-Scale Spanish Information Retrieval Dataset](https://mlsanity.com/paper/2409.05994/) (2024)
- [Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction](https://mlsanity.com/paper/2408.02337/) (2024)
- [Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching](https://mlsanity.com/paper/2402.14408/) (2024)
- [Polish ModernBERT: The Long and Short of Polish Language Understanding](https://mlsanity.com/paper/2609.01379/) (2026)

## Abstract (arXiv)

Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.