Update README.md

README.md

@@ -81,16 +81,15 @@ To collect human-annotated labels, we used Amazon Mechanical Turk (MTurk) to dep
 In each task, a single image was enlarged to 200 x 200 for clarity and presented alongside the question: `Choose any one "incorrect" label for this image`? Annotators were given four example labels to choose from (e.g., `dog, cat, ship, bird`), and were instructed to select the one that does not correctly describe the image.
 ## Citing
 
-If you find this package useful, please cite both the original works associated with each strategy and the following:
+If you find this dataset useful, please cite the following:
 
 ```
-@techreport{libcll2024,
-  author = {Nai-Xuan Ye and Tan-Ha Mai and Hsiu-Hsuan Wang and Wei-I Lin and Hsuan-Tien Lin},
-  title = {libcll: an Extendable Python Toolkit for Complementary-Label Learning},
-  institution = {National Taiwan University},
-  url = {https://github.com/ntucllab/libcll},
-  note = {available as arXiv preprint \url{https://arxiv.org/abs/2411.12276}},
-  month = nov,
-  year = 2024
+@article{
+  wang2024climage,
+  title={{CLI}mage: Human-Annotated Datasets for Complementary-Label Learning},
+  author={Hsiu-Hsuan Wang and Mai Tan Ha and Nai-Xuan Ye and Wei-I Lin and Hsuan-Tien Lin},
+  journal={Transactions on Machine Learning Research},
+  issn={2835-8856},
+  year={2025}
 }
 ```