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README.md

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-license: mit
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+---
+license: mit
+---
+
+# RAGU_Benchmarks
+## MultiQ Dataset
+
+### Dataset Description
+
+### Dataset Summary
+
+**MultiQ** is a small but rich dataset designed for question answering (QA) and multi-document information retrieval tasks. It contains 169 Russian-language questions, each accompanied by a correct answer and a set of relevant Wikipedia articles serving as context for locating the answer. This dataset is suitable for evaluating models’ ability to identify precise answers based on multiple potentially relevant documents. 
+
+Source: https://mera.a-ai.ru/ru/text/tasks/5
+
+The texts of the original benchmark were replaced with all Wikipedia articles relevant to the given question.
+### Dataset Structure
+
+Each example includes the following fields:
+
+- `index` - a unique numeric identifier for the example.
+- `question` - a Russian-language question requiring a concise, exact answer.
+- `answer` - the ground-truth answer (a string) corresponding to the question.
+- `articles` - a dictionary where keys are Wikipedia article titles and values are the full text of those articles. The articles contain sufficient information to deduce the correct answer.
+
+Example entry:
+```json
+{
+  "index": 0,
+  "question": "Где родился человек, который был братом Тиберия?",
+  "answer": "Рим",
+  "articles": {
+    "Тиберий Клавдий Нерон": "Тибе́рий Кла́вдий Не́рон — римский политический деятель..."
+  }
+}
+```
+
+### Dataset Statistics
+
+Total dataset size: **169 examples**
+
+- All examples belong to a single split (can be used as a test or validation set).
+- Questions span diverse topics: geography, history, politics, biographies.
+- Answers consist of short phrases or proper names.
+- Each question is supported by at least one Wikipedia article; often, only one highly relevant article is provided.
+
+### Dataset Creation
+
+#### Data Sources
+
+All articles are sourced from the Russian-language Wikipedia. Questions and answers were manually crafted to evaluate systems’ capability to extract factual information from the given context.
+
+#### Annotations
+
+Annotation includes:
+
+- Formulating questions that require specific factual responses.
+- Selecting a ground-truth answer verifiable within the article text.
+- Curating relevant article(s) containing the necessary information to derive the answer.
+
+Some questions may require logical inference or cross-referencing (e.g., “Tiberius’s brother → who is he? → where was he born?”).
+
+#### Language
+
+The entire dataset - including both questions and contexts - is in **Russian**.
+
+### Licensing Information
+
+Article texts are derived from **Wikipedia**, distributed under the **Creative Commons Attribution-ShareAlike (CC BY-SA)** license.  
+The dataset itself (its structure, questions, and answers) may be freely used for research and educational purposes. Attribution of the source is recommended when publishing results.
+
+## NSU QA Dataset
+
+### Dataset Description
+
+**NSU QA Dataset** is a specialized question answering (QA) and multi-document retrieval benchmark based on official materials, websites, and publications of Novosibirsk State University (NSU). The dataset contains 207 Russian-language questions, each paired with a precise answer and a list of relevant document IDs (pages) containing sufficient context to derive the answer. Suitable for evaluating models’ ability to extract factual information from structured and semi-structured university sources.
+
+#### Dataset Structure
+
+Each example includes the following fields:
+
+- `instruction` - question prompt (always starts with “Instruction” for standardization).
+- `inputs` - dictionary with key `'text'` containing the actual question; sometimes includes optional `'topic'`.
+- `outputs` - string with the correct, concise answer.
+- `meta` - metadata: unique `id`, `author`, `tour_name` (e.g., “Quest”).
+- `related_pages` - list of document IDs from the doc collection relevant for answering this question.
+
+Example entry:
+
+```json
+{
+  "instruction": "Instruction",
+  "inputs": {
+    "text": "Что такое направление подготовки «информатика и вычислительная техника»?"
+  },
+  "outputs": "Направление подготовки «Информатика и вычислительная техника» готовит специалистов в области разработки программного обеспечения, системного анализа и управления IT-проектами.",
+  "meta": {
+    "id": 3,
+    "author": "НГУ",
+    "tour_name": "Quest"
+  },
+  "related_pages": [230, 204, 1026, 948, 543]
+}
+```
+
+#### Document Collection (doc)
+
+A separate table contains 449 documents (`page_content`), each with a unique `id`, textual content, and `metadata` (including `title`). Also provided: `qa_references` - list of question IDs answerable using this document.
+
+Example document:
+
+```json
+{
+  "id": 21,
+  "page_content": "Механико-математический факультет НГУ\n\nНаправления подготовки: математика, механика, прикладная математика...",
+  "metadata": {
+    "title": "Образование и карьера на Механико-математическом факультете"
+  },
+  "qa_references": [116, 127]
+}
+```
+
+#### Dataset Statistics
+
+- Total questions: **207**
+- All questions are in **Russian**
+- Topics: education, university structure, admissions, faculties, programs, events, personnel, infrastructure
+- Answers: short, factual, sometimes marked as “no information available”
+- Each question linked to exactly 5 relevant documents (page IDs)
+- Documents cover a broad range: from applicant guides to research centers and contact pages
+
+#### Dataset Creation
+
+##### Data Sources
+
+All texts are derived from real NSU materials: official website, brochures, news, faculty descriptions, and program pages. No external sources (e.g., Wikipedia) are used.
+
+##### Annotations
+
+- Questions are designed to require specific, verifiable answers.
+- Answers are extracted directly from document texts or synthesized based on them.
+- For each question, 5 most relevant documents are manually selected.
+- Some questions intentionally have no answer in the provided fragments - to test model’s ability to handle “unknown” cases correctly.
+
+##### Language
+
+The entire dataset - questions, answers, and contexts - is exclusively in **Russian**.
+
+#### Licensing
+
+Texts are based on open NSU materials available on the official university website. Distributed under **CC BY 4.0**.
+The dataset itself (structure, annotations, markup) may be freely used for research and educational purposes with proper attribution.
+
+## CheGeKa Dataset (“What? Where? When?”)
+
+### Dataset Description
+
+#### Dataset Summary
+
+**CheGeKa** is a benchmark dataset designed to evaluate models’ ability to answer complex intellectual questions from the popular Russian TV quiz show “What? Where? When?”. The dataset contains 104 questions, each paired with a precise answer, metadata about the author and tournament, and references to related Wikipedia documents. It is suitable for testing fact retrieval, logical inference, and multi-document reasoning skills.
+
+All examples and contexts are in **Russian**, including question formulations and document content.
+
+Source: [https://mera.a-ai.ru/ru/text/tasks/8](https://mera.a-ai.ru/ru/text/tasks/8)
+#### Dataset Structure
+
+The dataset consists of two tables:
+
+##### Table `qa` - Questions and Answers:
+
+- `instruction` - prompt simulating participation in the game.
+- `inputs.text` - the actual question text.
+- `outputs` - ground-truth answer.
+- `meta` - metadata: `id`, `author`, `tour_name`, etc.
+- `related_pages` - list of document IDs from the `doc` table that provide relevant context.
+
+Example entry:
+
+```json
+{
+  "instruction": "You are participating in the quiz 'What? Where? When?'. Answer the question.",
+  "inputs": {
+    "text": "Автором текста гимна Норвегии является лауреат Нобелевской премии по литературе. Назовите его."
+  },
+  "outputs": "Бьёрнстьерне Бьёрнсон (лауреат Нобелевской премии по литературе 1903 года)",
+  "meta": {
+    "id": 0,
+    "author": "Орест Петросянц",
+    "tour_name": "Кубок Москвы 2005"
+  },
+  "related_pages": [0, 1]
+}
+```
+
+##### Table `documents` - Documents (Contexts):
+
+- `id` - unique document ID.
+- `page_content` - full text of the Wikipedia article.
+- `metadata.title` - article title.
+- `qa_references` - list of question IDs from `qa` that this document supports.
+
+Example entry:
+
+```json
+{
+  "id": 0,
+  "page_content": "Бьёрнстьерне Мартиниус Бьёрнсон (норв. Bjørnstjerne Martinus Bjørnson) - норвежский писатель, лауреат Нобелевской премии по литературе 1903 года...",
+  "metadata": {
+    "title": "Бьёрнстьерне Бьёрнсон",
+    "source": ""
+  },
+  "qa_references": [0]
+}
+```
+
+#### Dataset Statistics
+
+Total size: **104 questions**
+
+- All questions are in Russian.
+- Answers are short phrases, proper names, or titles, sometimes with clarifying notes in parentheses.
+- Each question is linked to 1-5 supporting Wikipedia articles.
+- Question authors include well-known CheGeKa writers (Orest Petrosyants, Evgeny Lyapin, Alexey Bogoslovsky, etc.).
+
+#### Dataset Creation
+
+##### Data Sources
+
+All documents are sourced from **Russian Wikipedia**. Questions and answers were collected from real tournaments of the “What? Where? When?” club held between 2000–2010.
+
+##### Annotations
+
+Annotation includes:
+
+- Formulating questions in the distinctive CheGeKa style - often metaphorical, culturally nuanced, or hint-based.
+- Selecting verifiable ground-truth answers supported by referenced documents.
+- Recording author and tournament metadata.
+- Linking each question to relevant Wikipedia articles.
+
+Some questions require not direct extraction but cultural knowledge or logical deduction.
+
+##### Language
+
+The entire dataset - including questions, answers, and contexts - is in **Russian**.
+
+#### Licensing Information
+
+Wikipedia article texts are distributed under the **Creative Commons Attribution-ShareAlike (CC BY-SA)** license.  
+The dataset structure, questions, and metadata may be freely used for research and educational purposes. Attribution is recommended when publishing results.