## 5 Discussion
### Evaluating Traditional NLP Models vs. LLMs in Fake News Detection  
* Research Question 1: are traditional NLP and CNN models or LLMs more accurate in fake news detection tasks? * Research Statement 1: fine-tuned LLMs outperform traditional NLP and CNN models in fake news detection, achieving near-perfect accuracy. In the comparative analysis of traditional NLP, CNN models, and LLMs in fake news identification tasks, in our study, several insights are underscored about model architecture, pretraining scope, and language comprehension capabilities. Fine-tuned LLMs, especially the GPT-4 Omni models, demonstrate a clear edge over both BERT-based models and CNN architectures, with accuracy levels reaching 98.6% for both GPT-4o and GPT-4o-mini. This near-perfect accuracy suggests that once fine-tuned, LLMs can spot fake news with high precision, making them valuable for tasks requiring fine semantic distinctions. The substantial margin over traditional models is particularly noteworthy because it showcases the advanced language understanding that LLMs bring to complex classification tasks, where subtle contextual cues determine the difference between legitimate content and false information. * The role of pretraining and architecture: Unlike CNNs, which are primarily designed for pattern recognition in structured data like images [45], LLMs are built with transformer-based architectures that allow for deep attention mechanisms and sequence-based learning. These transformers, pretrained on vast and diverse datasets, are adept at capturing language patterns, idiomatic expressions, and subtle semantic relationships. In fake news detection, this translates to a model that can understand nuanced phrasing or stylistic cues typical of misinformation, even when these cues are subtle or context-dependent. * CNN limitations: The CNN model (ft:cnn_adam) in this study achieved only 58.6% accuracy, which is markedly lower than the transformer-based models. CNNs are effective at identifying repetitive, structured patterns but fall short when tasked with understanding the complexities of human language, especially when misleading content relies on nuanced or indirect language. Since CNNs do not inherently process sequential information as effectively as transformers, they struggle to recognize the sequential and contextual patterns often necessary for distinguishing fake news. Furthermore, CNNs require substantial labeled data tailored to the target task to perform well in NLP tasks, given their lack of extensive pretraining on varied textual data [46]. * Comparing BERT and GPT models in fake news classification: The BERT model (ft:bert-adam), while achieving a respectable 97.5% accuracy, still fell short of the fine-tuned GPT-4 Omni models. This difference, although minor, may be attributed to the GPT-4 Omni models' extensive pretraining and perhaps larger scale compared to BERT. Additionally, while both BERT and GPTs are transformer-based, GPT models are autoregressive, which means they are trained to predict the next word in a sequence, potentially enhancing their understanding of sentence flow and structure--elements that are crucial for detecting deceptive or misleading language. BERT's bidirectional nature gives it a slight advantage in understanding context but might limit its proficiency in tasks requiring generation or classification of nuanced language. The findings suggest that transformer-based models, especially fine-tuned LLMs, are highly effective for fake news identification tasks. This has implications for organizations that require accurate and automated fake news classification, as LLMs can minimize false positives and false negatives more effectively than traditional models. The results also highlight that while BERT remains a viable option, GPT-based models offer a marginally higher level of performance, likely due to their extensive training on diverse language patterns.