# BRU Dataset: Balancing Rigor and Utility for Testing Cognitive Biases in LLMs

> 🧠 This dataset accompanies our paper **"Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions"**, **accepted at CogSci 2025**.

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## 📘 About the Dataset

The **BRU dataset** includes **205 multiple-choice questions**, each crafted to assess how LLMs handle well-known **cognitive biases**. Unlike widely used datasets such as [MMLU](https://arxiv.org/abs/2009.03300), [TruthfulQA](https://arxiv.org/abs/2109.07958), and [PIQA](https://arxiv.org/abs/1911.11641), BRU offers **comprehensive coverage of cognitive distortions**, rather than focusing solely on factual correctness or reasoning.

The dataset was developed through a multidisciplinary collaboration:
- An experienced **psychologist** designed the bias scenarios.
- A **medical data expert** ensured content validity.
- Two **NLP researchers** formatted the dataset for LLM evaluation.

Each question is backed by references to psychological literature and frameworks, with full documentation in the paper's appendix.

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## ✅ Covered Bias Categories

The dataset includes questions targeting the following **eight** types of cognitive biases:

- Anchoring Bias
- Base Rate Fallacy
- Conjunction Fallacy
- Gambler’s Fallacy
- Insensitivity to Sample Size
- Overconfidence Bias
- Regression Fallacy
- Sunk Cost Fallacy

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## 📂 Dataset Format

Each `.csv` file in this repository corresponds to one bias type. All files follow the same format:

| Question ID | Question Text | Ground Truth Answer |
|-------------|----------------|---------------------|
| 1           | (MCQ content)  | A                   |
| 2           | (MCQ content)  | C                   |
| ...         | ...            | ...                 |

- **First row**: column headers  
- **First column**: question number  
- **Second column**: question content (includes options)  
- **Third column**: correct answer label (e.g., A, B, C, D)

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## 📑 Citation

If you use the BRU dataset in your research, please cite our paper:

```bibtex
@inproceedings{zhong2025balancing,
title     = {Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions},
author    = {Zhong, H. and Wang, L. and Cao, Wenting and Sun, Zeyuan},
booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society},
volume    = {47},
year      = {2025},
publisher = {Cognitive Science Society},
url       = {https://escholarship.org/uc/item/2vr690cx}
}