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README.md
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---
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license: apache-2.0
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---
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license: apache-2.0
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task_categories:
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- text2text-generation
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- question-answering
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language:
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- tr
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size_categories:
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- 1K<n<10K
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---
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# Dataset Summary
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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.
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## Dataset Information
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- **Number of Instances**: 5999 (5725 synthetically generated question-answer pairs, 274 augmented negative samples)
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- **Dataset Size**: 1.2 MB
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- **Language**: Turkish
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- **Dataset License**: apache-2.0
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- **Dataset Category**: Text2Text Generation
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- **Dataset Domain**: STEM and Social Sciences
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## WikiRAG-TR Pipeline
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The creation of the dataset was accomplished in two main phases, each represented by a separate diagram.
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### Phase 1: Subcategory Collection
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In this initial phase:
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1. A curated list of seed categories was decided, including science, technology, engineering, mathematics, physics, chemistry, biology, geology, meteorology, history, social sciences, and more.
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2. Using these seed categories, subcategories were recursively gathered from Wikipedia.
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- **Recursion depth** was set to 3 and the **number of subcategories** to collect was limited to 100 for each depth layer.
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3. For each step, following subcategory types were filtered out:
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- Subcategories containing **NSFW words**.
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- Subcategories that only contain **lists of items**
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- Subcategories used as **templates**
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4. Articles from the resulting subcategory list were acquired.
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### Phase 2: Dataset Generation
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The second phase involved the following steps:
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1. Introduction sections were extracted from the articles gathered in Phase 1.
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- If the introduction was **too short** or **too long** (less than 50 or more than 2500 characters), the article was discarded.
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- If the introduction contained **NSFW words**, the article was discarded.
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- If the introduction contained **equations**, the article was discarded.
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- If the introduction section was **empty**, the article was discarded.
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2. The filtered introductions were fed into a large language model to generate synthetic question and answer pairs.
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3. For each resulting row in the dataset (containing an introduction, question, and answer), the following operations were performed:
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- Unrelated contexts (introductions) were gathered from other rows to add false positive retrievals to the context.
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- These unrelated contexts were appended to a list.
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- 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.)
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- The list was shuffled to **randomize the position** of the relevant context.
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- The list elements were joined using the '\n' character.
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## Considerations for Using the Data
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The generated answers are usually short and concise. This may lead to models trained on this dataset to generate short answers.
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## Dataset Columns
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- `id`: Unique identifier for each row.
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- `question`: The question generated by the model.
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- `answer`: The answer generated by the model.
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- `context`: The augmented context containing both relevant and irrelevant information.
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- `is_negative_response`: Indicates whether the answer is a negative response (0: No, 1: Yes).
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- `number_of_articles`: The number of article introductions used to create the context.
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# Attributions
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<a href="https://www.flaticon.com/free-icons/globe" title="globe icons">Globe icons created by Freepik - Flaticon</a>
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<a href="https://www.flaticon.com/free-icons/search" title="search icons">Search icons created by Freepik - Flaticon</a>tle="search icons">Search icons created by Freepik - Flaticon</a>
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