Update README.md

README.md

@@ -8,48 +8,92 @@ configs:
 - config_name: train
   data_files:
   - split: 2ppl
-    path: 
-    - "train/people2_num200.jsonl"
+    path:
+    - train/people2_num200.jsonl
   - split: 3ppl
-    path: 
-    - "train/people3_num1000.jsonl"
+    path:
+    - train/people3_num1000.jsonl
   - split: 4ppl
-    path: 
-    - "train/people4_num1000.jsonl"
+    path:
+    - train/people4_num1000.jsonl
   - split: 5ppl
-    path: 
-    - "train/people5_num1000.jsonl"
+    path:
+    - train/people5_num1000.jsonl
   - split: 6ppl
-    path: 
-    - "train/people6_num1000.jsonl"
+    path:
+    - train/people6_num1000.jsonl
   - split: 7ppl
-    path: 
-    - "train/people7_num1000.jsonl"
+    path:
+    - train/people7_num1000.jsonl
   - split: 8ppl
-    path: 
-    - "train/people8_num1000.jsonl"
+    path:
+    - train/people8_num1000.jsonl
 - config_name: test
   data_files:
   - split: 2ppl
-    path: 
-    - "test/people2_num100.jsonl"
+    path:
+    - test/people2_num100.jsonl
   - split: 3ppl
     path:
-    - "test/people3_num100.jsonl"
+    - test/people3_num100.jsonl
   - split: 4ppl
     path:
-    - "test/people4_num100.jsonl"
+    - test/people4_num100.jsonl
   - split: 5ppl
     path:
-    - "test/people5_num100.jsonl"
+    - test/people5_num100.jsonl
   - split: 6ppl
     path:
-    - "test/people6_num100.jsonl"
+    - test/people6_num100.jsonl
   - split: 7ppl
     path:
-    - "test/people7_num100.jsonl"
+    - test/people7_num100.jsonl
   - split: 8ppl
     path:
-    - "test/people8_num100.jsonl"
- 
+    - 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}, 
+}
+```