---
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"
}
```