Updated the bib file

README.md

@@ -44,12 +44,23 @@ For more details, refer to the [original paper](https://arxiv.org/abs/2409.12060
 If you use this dataset, please cite it using the following BibTeX entry:
 
 ```bibtex
-@misc{michail2024paraphrasuscomprehensivebenchmark,
-      title={PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection Models}, 
-      author={Andrianos Michail and Simon Clematide and Juri Opitz},
-      year={2024},
-      eprint={2409.12060},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2409.12060}, 
+@inproceedings{michail-etal-2025-paraphrasus,
+    title = "{PARAPHRASUS}: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models",
+    author = "Michail, Andrianos  and
+      Clematide, Simon  and
+      Opitz, Juri",
+    editor = "Rambow, Owen  and
+      Wanner, Leo  and
+      Apidianaki, Marianna  and
+      Al-Khalifa, Hend  and
+      Eugenio, Barbara Di  and
+      Schockaert, Steven",
+    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
+    month = jan,
+    year = "2025",
+    address = "Abu Dhabi, UAE",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.coling-main.585/",
+    pages = "8749--8762",
+    abstract = "The task of determining whether two texts are paraphrases has long been a challenge in NLP. However, the prevailing notion of paraphrase is often quite simplistic, offering only a limited view of the vast spectrum of paraphrase phenomena. Indeed, we find that evaluating models in a paraphrase dataset can leave uncertainty about their true semantic understanding. To alleviate this, we create PARAPHRASUS, a benchmark designed for multi-dimensional assessment, benchmarking and selection of paraphrase detection models. We find that paraphrase detection models under our fine-grained evaluation lens exhibit trade-offs that cannot be captured through a single classification dataset. Furthermore, PARAPHRASUS allows prompt calibration for different use cases, tailoring LLM models to specific strictness levels. PARAPHRASUS includes 3 challenges spanning over 10 datasets, including 8 repurposed and 2 newly annotated; we release it along with a benchmarking library at https://github.com/impresso/paraphrasus"
 }