README.md

---
license: apache-2.0
task_categories:
- text2text-generation
- question-answering
language:
- tr
size_categories:
- 1K<n<10K
---

![WikiRAG-TR Icon](docs/WikiRAG_TR.png)

# Dataset Summary
WikiRAG-TR is a dataset of 6K (5999) question and answer pairs which synthetically created from introduction part of Turkish Wikipedia Articles. The dataset is created to be used for Turkish Retrieval-Augmented Generation (RAG) tasks.

## Dataset Information
- **Number of Instances**: 5999 (5725 synthetically generated question-answer pairs, 274 augmented negative samples)
- **Dataset Size**: 20.5 MB
- **Language**: Turkish
- **Dataset License**: apache-2.0
- **Dataset Category**: Text2Text Generation
- **Dataset Domain**: STEM and Social Sciences

## WikiRAG-TR Pipeline

The creation of the dataset was accomplished in two main phases, each represented by a separate diagram.

### Phase 1: Subcategory Collection
![Subcategory collection diagram](docs/collecting_subcategories.png)

In this initial phase:
1. A curated list of seed categories was decided, including science, technology, engineering, mathematics, physics, chemistry, biology, geology, meteorology, history, social sciences, and more.
2. Using these seed categories, subcategories were recursively gathered from Wikipedia.
    - **Recursion depth** was set to 3 and the **number of subcategories** to collect was limited to 100 for each depth layer.
3. For each step, following subcategory types were filtered out:
    - Subcategories containing **NSFW words**.
    - Subcategories that only contain **lists of items**
    - Subcategories used as **templates**
4. Articles from the resulting subcategory list were acquired.

### Phase 2: Dataset Generation
![Creating WikiRAG-TR Diagram](docs/creating_wikirag_tr.png)

The second phase involved the following steps:
1. Introduction sections were extracted from the articles gathered in Phase 1.
    - If the introduction was **too short** or **too long** (less than 50 or more than 2500 characters), the article was discarded.
    - If the introduction contained **NSFW words**, the article was discarded.
    - If the introduction contained **equations**, the article was discarded.
    - If the introduction section was **empty**, the article was discarded.
2. The filtered introductions were fed into a large language model `(Gemma-2-27B-it)` to generate synthetic question and answer pairs.
3. For each resulting row in the dataset (containing an introduction, question, and answer), the following operations were performed:
    - Unrelated contexts (introductions) were gathered from other rows to add false positive retrievals to the context.
    - These unrelated contexts were appended to a list.
    - The related context was added to this list. (In some cases, the relevant context was omitted to create **negative samples** where the answer indicates the model can't answer the question due to insufficient information. These negative samples were created separately, ensuring all original questions have corresponding answers.)
    - The list was shuffled to **randomize the position** of the relevant context.
    - The list elements were joined using the '\n' character.

## Considerations for Using the Data
The generated answers are usually short and concise. This may lead to models trained on this dataset to generate short answers.
Since Wikipedia articles were used to create this dataset, any biases and inaccuracies present in them may also exist in this dataset.

## Dataset Columns
- `id`: Unique identifier for each row.
- `question`: The question generated by the model.
- `answer`: The answer generated by the model.
- `context`: The augmented context containing both relevant and irrelevant information.
- `is_negative_response`: Indicates whether the answer is a negative response (0: No, 1: Yes).
- `number_of_articles`: The number of article introductions used to create the context.

# Attributions
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