Update README.md

README.md

@@ -70,7 +70,7 @@ from datasets import load_dataset
 
 dataset = load_dataset("your-hf-username/combined-dataset")
 print(dataset['train'][0])
-
+```
 
 # [Your Model/Dataset Name]
 
@@ -85,33 +85,13 @@ This resource accompanies our paper accepted in the **Late Breaking Work** track
 
 ## ✨ 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.
+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
 
@@ -128,4 +108,3 @@ more heterogeneous and representative data with human-in-theloop, for better mod
   note      = {Accepted. Session [XX], Paper ID [XYZ]},
   url       = {https://2025.hci.international/proceedings.html}
 }
-