Update README.md

README.md

@@ -70,3 +70,62 @@ from datasets import load_dataset
 
 dataset = load_dataset("your-hf-username/combined-dataset")
 print(dataset['train'][0])
+
+
+# [Your Model/Dataset Name]
+
+This resource accompanies our paper accepted in the **Late Breaking Work** track of **HCI International 2025**.
+
+📄 **Paper Title:** _"Your Paper Title Here"_  
+👩‍💻 **Authors:** Naseem Machlovi, [Other Authors]  
+📍 **Conference:** HCI International 2025 – Late Breaking Work  
+🔗 [Link to Proceedings](https://2025.hci.international/proceedings.html)
+
+---
+
+## ✨ Description
+
+As AI systems become more integrated into daily
+life, the need for safer and more reliable moderation has never
+been greater. Large Language Models (LLMs) have demonstrated
+remarkable capabilities, surpassing earlier models in complexity
+and performance. Their evaluation across diverse tasks has
+consistently showcased their potential, enabling the development
+of adaptive and personalized agents. However, despite these
+advancements, LLMs remain prone to errors, particularly in
+areas requiring nuanced moral reasoning. They struggle with
+detecting implicit hate, offensive language, and gender biases
+due to the subjective and context-dependent nature of these
+issues. Moreover, their reliance on training data can inadvertently
+reinforce societal biases, leading to inconsistencies and ethical
+concerns in their outputs. To explore the limitations of LLMs
+in this role, we developed an experimental framework based
+on state-of-the-art (SOTA) models to assess human emotions
+and offensive behaviors. The framework introduces a unified
+benchmark dataset encompassing 49 distinct categories spanning
+the wide spectrum of human emotions, offensive and hateful
+text, and gender and racial biases. Furthermore, we introduced
+SafePhi, a QLoRA fine-tuned version of Phi-4, adapting diverse
+ethical contexts and outperforming benchmark moderators by
+achieving a Marco F1 score of 0.89, where OpenAI Moderator
+and Llama Guard score 0.77 and 0.74, respectively. This research
+also highlights the critical domains where LLM moderators
+consistently underperformed, pressing the need to incorporate
+more heterogeneous and representative data with human-in-theloop, for better model robustness and explainability.
+
+## 🚀 Usage
+
+[Code snippets or sample usage if it's a model or dataset.]
+
+## 📖 Citation
+
+```bibtex
+@inproceedings{machlovi2025hci,
+  title     = {Towards Safer AI Moderation: Evaluating LLM Moderators Through a Unified Benchmark Dataset and Advocating a Human-First Approach},
+  author    = {Naseem Machlovi and ...},
+  booktitle = {HCI International 2025 Late Breaking Work – Proceedings},
+  year      = {2025},
+  note      = {Accepted. Session [XX], Paper ID [XYZ]},
+  url       = {https://2025.hci.international/proceedings.html}
+}
+