Update README.md

README.md

@@ -3,7 +3,7 @@ license: cc
 language:
 - en
 ---
----
+
 # Dataset Card for PS-Eval Dataset
 
 ## Dataset Summary
@@ -64,7 +64,7 @@ The dataset is provided as a single split with **1,112 samples**:
 
 ## Dataset Creation
 ### Source Data
-The PS-Eval dataset is built on top of the **WiC Dataset** (Word-in-Context) – a rich resource for polysemous words originally introduced in \citet{pilehvar2019wic}.
+The PS-Eval dataset is built on top of the **WiC Dataset** (Word-in-Context) – a rich resource for polysemous words originally introduced in [Pilehvar and Camacho-Collados (2019)](https://arxiv.org/abs/1808.09121).
 
 ### Filtering Process
 We carefully selected instances from WiC where the target word is tokenized as a **single token** in GPT-2-small. This ensures consistency when analyzing activations in Sparse Autoencoders.
@@ -100,7 +100,9 @@ The dataset supports evaluation metrics such as:
 - **F1 Score**
 - **Specificity**
 
-These metrics are particularly important for evaluating polysemy detection models and Sparse Autoencoders.
+These metrics are particularly important for evaluating polysemy detection models and Sparse Autoencoders. 
+
+For implementation details of the evaluation metrics, please refer to the GitHub repository: **[link_to_your_repo]**.
 
 ## Dataset Curators
 This dataset was curated by **Gouki Minegishi** as part of research on polysemantic activation analysis in Sparse Autoencoders and interpretability for large language models.
@@ -109,11 +111,10 @@ This dataset was curated by **Gouki Minegishi** as part of research on polyseman
 If you use the PS-Eval Dataset in your work, please cite:
 
 ```
-@inproceedings{your2025ps_eval,
-  title={hoge},
-  author={hoge},
-  booktitle={hoge},
-  year={2025}
+@inproceedings{minegishi2024ps-eval,
+  title={Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words},
+  author={Gouki Minegishi, Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo},
+  year={2024},
+  url={hoge}
 }
 ```
-