Update README.md

README.md

@@ -13,6 +13,8 @@ tags:
 
 # LLaMA2 Fine-Tuned on not Engaging with Hate Speech
 
+This model was created as part of the work "Decoding Hate: Exploring Language Models' Reactions to Hate Speech," which was accepted for the main conference of NAACL 2025.
+
 ## Model Description
 This model is a fine-tuned version of `meta-llama/Llama-2-13b-chat-hf` on a hate speech dataset using the PEFT approach, to prevent the model from exacerbating hate discourse.
 
@@ -113,18 +115,14 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 If you use this model, please cite the following reference:
 
 ```bibtex
-@article{Piot_Martín-Rodilla_Parapar_2024,
-  title={MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection},
-  volume={18},
-  url={https://ojs.aaai.org/index.php/ICWSM/article/view/31445},
-  DOI={10.1609/icwsm.v18i1.31445},
-  abstractNote={Hate speech represents a pervasive and detrimental form of online discourse, often manifested through an array of slurs, from hateful tweets to defamatory posts. As such speech proliferates, it connects people globally and poses significant social, psychological, and occasionally physical threats to targeted individuals and communities. Current computational linguistic approaches for tackling this phenomenon rely on labelled social media datasets for training. For unifying efforts, our study advances in the critical need for a comprehensive meta-collection, advocating for an extensive dataset to help counteract this problem effectively. We scrutinized over 60 datasets, selectively integrating those pertinent into MetaHate. This paper offers a detailed examination of existing collections, highlighting their strengths and limitations. Our findings contribute to a deeper understanding of the existing datasets, paving the way for training more robust and adaptable models. These enhanced models are essential for effectively combating the dynamic and complex nature of hate speech in the digital realm.},
-  number={1},
-  journal={Proceedings of the International AAAI Conference on Web and Social Media},
-  author={Piot, Paloma and Martín-Rodilla, Patricia and Parapar, Javier},
-  year={2024},
-  month={May},
-  pages={2025-2039}
+@misc{piot2024decodinghateexploringlanguage,
+      title={Decoding Hate: Exploring Language Models' Reactions to Hate Speech}, 
+      author={Paloma Piot and Javier Parapar},
+      year={2024},
+      eprint={2410.00775},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.00775}, 
 }
 ```