## 4 Results and Discussion
### Comparative Analysis with Recent Studies  
To frame our findings in reference to the existing literature, we compared our results with a few recent studies focusing on hate speech and offensive language detection utilizing machine learning and LLMs, as indicated in Table 4. Instead, this table shows tons of approaches, languages, etc., some of which are completely different with respect to F2M, and some are M2F with transformers. For instance, [30] adapted LLaMA 2 to classify sexually predatory and abusive text, and [31] proposed en-MBERT multilingual for mBERT. However both studies did not report F1-scores explicitly, preventing direct comparison. Meanwhile, [32] reported a 96% F1-score using BERT, which is remarkable, and [33] proposed a CNN-gram architecture for Roman Urdu which reached an F1-score of 88%.  
In contrast, our study outperforms many of these benchmarks by reporting a 96.58% F1-score with LLaMA 2, fine-tuned with LoRA on a Roman Urdu dataset that is manually annotated from YouTube comments, as can be seen in Table 5. This performance is on par with the best reported results and demonstrates the effectiveness of LoRA-based fine-tuning on low-resource code-mixed languages such as Roman Urdu. Other papers like [36] indicate that GPT-3 and BERT perform differently on hate speech detection--a process that greatly benefits from supervised fine-tuning and domain adaptation. Our work is distinctive in that it develops a unified framework for Roman Urdu offensive language detection involving traditionally used machine learning (ML) and deep learning (DL) models as well transformer models, all of which we benchmark holistically--a task that is still relatively untouched but which is critically helpful at a societal level.  
In general, this study shows that CNNs are a good choice for offensive language detection of Roman Urdu, but also that LLaMA 2 (fine-Tuned with LoRA) significantly outperforms all other models that are considered very good options for this classification scenario. These values suggest that the CNN established a good fit with the dataset, as the model captured complex text patterns in the data through effective combination of n-gram features. Yet, the classification performance was further improved with the advent of fine-tuned transformer models. LLaMA 2 achieves an F1-score of 96.58% and an accuracy of 96.50%, outperforming the CNN, demonstrating its superior ability to capture deeper linguistic nuances and contextual dependencies present in the Roman Urdu text.  
Figure 5: LLMs Confusion matrix.  
This is indicative of the robustness of tuned transformer architectures, such as LLaMA 2 with LoRA, against the complexity of transliterated and non-standardized text. ModernBERT (M-BERT) also performs comparably (=94.10% F1-score) but still cannot beat LLaMA 2. The results show that fine-tuned transformers provide better offensive language detection compared with the traditional ML and DL models. These results indicate that fine-tuned LLMs are the best-suited baseline for text classification of heterogeneous and complex linguistic patterns and thus are ready for real-world content moderation implementations in Roman Urdu.