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license: mit
tags:
- benchmark
task_categories:
- multiple-choice
- question-answering
---
# QUAD-Bench: A Lightweight AI Reasoning & Acuity Benchmark
**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**).
---
## 📊 Dataset Structure
The benchmark consists of 100 questions divided into 4 categories (25 questions each):
| 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` |
---
## 📁 JSON Format
Each entry in `dataset.json` follows this simple schema:
```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"
}
``` |