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---
license: mit
language:
- ru
tags:
- text
- datasets
size_categories:
- 10K<n<100K
<<<<<<< HEAD
=======
configs:
- config_name: default
data_files: open_questions_data.json
config_name: default
features:
- name: instruction
dtype: string
- name: inputs
struct:
- name: task
dtype: string
- name: text
dtype: string
- name: options
struct:
- name: option_1
dtype: string
- name: option_2
dtype: string
- name: option_3
dtype: string
- name: option_4
dtype: string
- name: option_5
dtype: string
- name: option_6
dtype: string
- name: option_7
dtype: string
- name: option_8
dtype: string
- name: option_9
dtype: string
- name: outputs
dtype: string
- name: meta
struct:
- name: subject
dtype: string
- name: type
dtype: string
- name: source
dtype: string
- name: comment
dtype: string
- name: provac_score
dtype: int32
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---
# SLAVA: A benchmark of the **S**ocio-political **L**andscape **A**nd **V**alue **A**nalysis
<div align="center">
<a href="https://huggingface.co/spaces/RANEPA-ai/SLAVA">
<img src="https://raw.githubusercontent.com/Y1OV/project_lab/refs/heads/main/data/logo.jpg" alt="Foo" style="width: 50%; height: auto;">
</a>
</div align="center">
## Dataset Description
Since 2024, the SLAVA benchmark has been developed, containing about 14,000 questions focused on the Russian domain, covering areas such as history, political science, sociology, political geography, and national security basics. This benchmark evaluates the ability of large language models (LLMs) to handle sensitive topics important to the Russian information space.
#### Main tasks:
- Testing the factual knowledge of LLMs in Russian domains.
- Assessing the sensitivity (provocativeness) of the questions.
- Creating a comprehensive evaluation system based on answer accuracy.
#### Structure:
The questions are divided into the following types:
- Multiple choice with one or several correct answers.
- Sequences and matching.
- Open-ended responses.
#### Question provocativeness:
- **1 point**: Low sensitivity — generally accepted facts.
- **2 points**: Medium sensitivity — controversial topics.
- **3 points**: High sensitivity — political and cultural issues that provoke conflicts.
#### Results:
24 LLMs supporting the Russian language were tested. Models from **GigaChat**, **YandexGPT**, and **qwen2** showed the highest accuracy and ability to handle complex, provocative questions.
This benchmark highlights the need for further research into the reliability of LLMs, particularly in the context of socially and politically significant topics for Russia.
## Dataset Composition
### Data Instances
```
{
"instruction": "Прочитайте приведённые далее задачу, текст и выполните по ним задание.\n Задача: {task}\n Текст: {text}\n Кратко сформулируйте и запишите ответ на задание (слово, словосочетание, одно или несколько предложений).",
"inputs": {
"task": "В приведенном ряду найдите понятие, которое является обобщающим для всех остальных представленных понятий. Запишите в ответ это слово (словосочетание).",
"text": "Выборы, референдум, форма демократии, сход граждан, прямая демократия.",
"options": {
"option_1": NaN,
"option_2": NaN,
"option_3": NaN,
"option_4": NaN,
"option_5": NaN,
"option_6": NaN,
"option_7": NaN,
"option_8": NaN,
"option_9": NaN
}
},
"outputs": "форма демократии",
"meta": {
"subject": "Обществознание",
"type": "открытый ответ",
"source": "https://socege.sdamgia.ru/problem?id=11291",
"comment": "Д2",
"provac_score": 2
}
}
```
### Data Fields
- instruction: A string containing the instructions that explain what needs to be done in the task.
- inputs:
- task: A string containing the formulation of the task.
- text: A string with the main text or phrase for which a response needs to be selected.
- options: An object containing a list of possible answer choices:
- option_1 - option_9: Answer choices represented as strings. If there are fewer options, unused fields may contain null.
- outputs: A number indicating the correct answer choice (answer option number).
- meta: Additional information about the task:
- subject: A string specifying the subject of the task (e.g., History).
- type: A string describing the type of task (e.g., multiple choice).
- source: A string containing the source of the task.
- comment: A field for comments (can be null if no comments are present).
- provac_score: A numerical value indicating the difficulty or importance of the task.
## How to Download
```
from huggingface_hub import hf_hub_download
dataset = hf_hub_download(repo_id="RANEPA-ai/SLAVA-OpenData-2800-v1", filename="open_questions_data.json", repo_type="dataset")
filename="open_questions_data.json", repo_type="dataset")
```
## Licensing Information
#### ⚖ MIT license
## Citation Information
```
@misc{SLAVA: Benchmark of Sociopolitical Landscape and Value Analysis,
author = {A. S. Chetvergov,
R. S. Sharafetdinov,
M. M. Polukoshko,
V. A. Akhmetov,
N. A. Oruzheynikova,
E. S. Anichkov,
S. V. Bolovtsov},
title = {SLAVA: Benchmark of Sociopolitical Landscape and Value Analysis (2024)},
year = {2024},
publisher = {Hugging Face},
howpublished = "\url{https://huggingface.co/spaces/RANEPA-ai/SLAVA}"
}
``` |