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| license: mit |
| tags: |
| - benchmark |
| task_categories: |
| - multiple-choice |
| - question-answering |
| --- |
| # QUAD-Bench: A Lightweight AI Reasoning & Acuity Benchmark |
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| **QUAD-Bench** is a compact, multiple-choice benchmark dataset designed for quick evaluation of Large Language Models (LLMs). The dataset contains 100 questions evenly distributed across 4 fundamental capabilities, requiring the model to select exactly one correct answer option (**A**, **B**, **C**, or **D**). |
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| ## 📊 Dataset Structure |
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| The benchmark consists of 100 questions divided into 4 categories (25 questions each): |
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| | Category | Description | Question Range | |
| | :--- | :--- | :--- | |
| | **Logic** | Logical deductions, conditional statements, syllogisms, and classic riddles. | `id: 1–25` | |
| | **Attention** | Pattern recognition, string manipulation, character counting, and detail sensitivity. | `id: 26–50` | |
| | **Math** | Basic arithmetic, algebra, probability, geometry, and calculus concepts. | `id: 51–75` | |
| | **Code** | Syntax understanding, algorithm complexities, data structures, and tech fundamentals. | `id: 76–100` | |
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| ## 📁 JSON Format |
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| Each entry in `dataset.json` follows this simple schema: |
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| ```json |
| { |
| "id": 1, |
| "category": "logic", |
| "question": "If all Bloops are Razzies and all Razzies are Lazzies, then all Bloops are definitely Lazzies.\nA) True\nB) False\nC) Cannot be determined\nD) None of the above", |
| "answer": "A" |
| } |
| ``` |
