## 6 Conclusions and Future Work  
We have employed various approaches for offensive language detection in our study. The first approach utilizes baseline ML models, including RF, LR, SVM, and Naive Bayes, coupled with different feature extraction techniques such as TF-IDF, Word2 Vec, FKRA/FRE, and VADER sentiment scores. In the second approach, we explored the use of GloVe embeddings with varying dimensions, such as 50d, 100d, 200d, and 300d, in combination with 1D Convolutional Neural Networks (1D CNN) and MLP for the task. In the third proposed approach, we incorporated a transformer model as an encoder and combined it with DL models to classify OL. In this approach, we have included BERT base model as preprocessor and encoder, which helped in capturing the context for addressing the problem. Our work focuses on efficient automatic detection of OL. We initially explored the importance of features using baseline ML models and subsequently conducted experiments with DL models and transformer encoders to analyze the results. Our findings emphasized the pivotal role of features in the detection of OL, also highlighting the effectiveness of embedding-based features. However, it is essential to acknowledge potential biases in embedding features. The transformer model proved to be highly efficient in this task, but it comes with trade-offs such as longer training time and increased memory requirements. Our research serves as a valuable baseline and provides insights for future work in the realm of enhancing the efficient detection of OL. We recommend considering user-specific features to improve detection accuracy. Additionally, there is room for further research in detecting implicit hate and identifying the target of OL. Some other future work plans include the exploration of quantization techniques in conjunction with Large Language Models (LLMs) to enhance the efficient detection of OL. Additionally, we aim to tackle the issue of limited dataset availability by fine-tuning LLMs with smaller datasets, potentially mitigating the challenges presented due to the scarcity of large datasets in this domain. Quantization techniques [73] involve reducing the precision of model parameters, which decreases memory usage and computational requirements during inference without significant performance loss. Applying quantization to transformer-based models like BERT, ALBERT, and ELECTRA BERT can enhance efficiency by reducing their memory footprint, enabling deployment on resource-constrained platforms like mobile or edge devices. Though quantization may slightly degrade performance, careful optimization can mitigate this impact. Fine-tuning LLMs [74] involves updating pre-trained model parameters on a task-specific dataset with few labeled examples. This adaptation allows the model to learn domain-specific offensive language patterns, leveraging pre-trained knowledge. Fine-tuning with smaller datasets is particularly beneficial when annotated data are limited or expensive to obtain. Despite limited data, fine-tuning can significantly improve offensive language detection performance by adapting pre-trained representations to the target domain's nuances. However, regularization techniques are essential to prevent overfitting. **Author Contributions:** Conceptualization, A.Y. and V.S. ; methodology, A.Y. ; software, A.Y. ; validation, F.A.K., A.Y. and V.S. ; formal analysis, A.Y. ; investigation, A.Y., F.A.K. and V.S. ; resources, F.A.K. ; data curation, A.Y. and V.S. ; writing--original draft preparation, A.Y. ; writing--review and editing, F.A.K. and V.S. ; visualization, A.Y. ; supervision, V.S. ; project administration, F.A.K. ; funding acquisition, F.A.K. All authors have read and agreed to the published version of the manuscript. **Funding:** The funding for this work was provided by King Saud University, Riyadh, Saudi Arabia, under Researchers Supporting Project number RSPD2024R1062.