# A Russian Jeopardy! Data Set for Question-Answering Systems

arXiv: https://arxiv.org/abs/2112.02325 · HTML: https://mlsanity.com/paper/2112.02325/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2010.15925/) (2020)
- [SberQuAD -- Russian Reading Comprehension Dataset: Description and Analysis](https://mlsanity.com/paper/1912.09723/) (2019)
- [What Question Answering can Learn from Trivia Nerds](https://mlsanity.com/paper/1910.14464/) (2019)
- [RuBQ: A Russian Dataset for Question Answering over Wikidata](https://mlsanity.com/paper/2005.10659/) (2020)

## Built on by (most cited first, 3 of 3)

- [TAPE: Assessing Few-shot Russian Language Understanding](https://mlsanity.com/paper/2210.12813/) (2022)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [Time Capsule of Testable Human Knowledge: 41 Years of Jeopardy! in a Single Free Local Model](https://mlsanity.com/paper/2608.27459/) (2026)

## Abstract (arXiv)

Question answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much appreciated in chatbots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy!-like Russian QA data set collected from the official Russian quiz database Chgk (che ge ka). The data set includes 379,284 quiz-like questions with 29,375 from the Russian analogue of Jeopardy! - "Own Game". We observe its linguistic features and the related QA-task. We conclude about perspectives of a QA competition based on the data set collected from this database.