---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
language:
- en
configs:
- config_name: train
  data_files:
  - split: 2ppl
    path:
    - train/people2_num200.jsonl
  - split: 3ppl
    path:
    - train/people3_num1000.jsonl
  - split: 4ppl
    path:
    - train/people4_num1000.jsonl
  - split: 5ppl
    path:
    - train/people5_num1000.jsonl
  - split: 6ppl
    path:
    - train/people6_num1000.jsonl
  - split: 7ppl
    path:
    - train/people7_num1000.jsonl
  - split: 8ppl
    path:
    - train/people8_num1000.jsonl
- config_name: test
  data_files:
  - split: 2ppl
    path:
    - test/people2_num100.jsonl
  - split: 3ppl
    path:
    - test/people3_num100.jsonl
  - split: 4ppl
    path:
    - test/people4_num100.jsonl
  - split: 5ppl
    path:
    - test/people5_num100.jsonl
  - split: 6ppl
    path:
    - test/people6_num100.jsonl
  - split: 7ppl
    path:
    - test/people7_num100.jsonl
  - split: 8ppl
    path:
    - test/people8_num100.jsonl
tags:
- logical
- reasoning
pretty_name: K
size_categories:
- 1K<n<10K
---



# 📘 knights-and-knaves Dataset [Project Page](https://memkklogic.github.io/)

The **knights-and-knaves dataset** serves as a logical reasoning benchmark to evaluate the reasoning capabilities of LLMs.

**Check out the [perturbed knights-and-knaves dataset](https://huggingface.co./datasets/K-and-K/perturbed-knights-and-knaves)** to evaluate the memorization of LLMs in reasoning! 

## Loading the dataset

To load the dataset:

```python
from datasets import load_dataset
data_subject = load_dataset('K-and-K/knights-and-knaves','test',split="2ppl")
```
* Available subset: `test`, `train`.
* Available split: `2ppl`,`3ppl`,`4ppl`,`5ppl`,`6ppl`,`7ppl`,`8ppl`.

## 🛠️ Codebase

To evaluate LLMs on our datasets, visit our [GitHub repository](https://github.com/AlphaPav/mem-kk-logic/).

## ⭐ Citing our Work

If you find our codebase and datasets beneficial, kindly cite our work:

```bibtex
@article{xie2024memorization,
title={On Memorization of Large Language Models in Logical Reasoning}, 
author={Chulin Xie and Yangsibo Huang and Chiyuan Zhang and Da Yu and Xinyun Chen and Bill Yuchen Lin and Bo Li and Badih Ghazi and Ravi Kumar},
year={2024},
eprint={2410.23123},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.23123}, 
}
```