## 5 Discussion
### Pre-Fine-Tuning Performance Assessment Within the GPT-4 Omni Family  
* Research Question 2: among the GPT-4 Omni family, which model performs best prior to fine-tuning?
* Research Statement 2: prior to fine-tuning, GPT-4 Omni models perform poorly in fake news detection, highlighting the necessity for task-specific training.  
Our analysis of the GPT-4 Omni family models before fine-tuning reveals several key insights about their performance capabilities, limitations, and the nature of zero-shot learning in specialized tasks like fraudulent news detection.  
* Baseline performance and lack of task-specific knowledge: The low accuracy scores of 12.3% for GPT-4o and 24.3% for GPT-4o-mini underscore that both models lack the task-specific knowledge required for effective fake news detection in a zero-shot setting. These results suggest that while LLMs have extensive general language understanding, applying this to a nuanced, specialized task like misleading news categorization is challenging without specific tuning. Fake news classification often relies on recognizing subtle cues, phrasing patterns, and contextual red flags that are challenging for general-purpose models to identify without tailored training.
* The performance gap between the models--specifically, the nearly 50% difference in accuracy between GPT-4o and GPT-4o-mini--was unexpected. A plausible hypothesis is that GPT-4o, despite being larger and more powerful, may have been overfitted to its training data. This could cause the model to struggle in a zero-shot classification task if it overly relies on patterns from the training set that do not generalize well to new data. On the other hand, GPT-4o-mini, with its smaller parameter size, might have avoided overfitting, leading to better generalization in a zero-shot setting. It is a fact that sometimes smaller models can perform better in certain tasks because they learn to prioritize the most important features and avoid distractions from irrelevant data, while larger models might become bogged down by unnecessary complexity [47].
* Challenges of zero-shot fake news detection: Fake news detection is a complex task that requires not only general language understanding but also the ability to differentiate between legitimate and deceptive communication. Fraudulent content often imitates legitimate language, which makes it difficult to classify correctly without exposure to examples during training [40]. Zero-shot models, despite their general versatility, lack the fine-grained knowledge to identify these distinctions [48]. This is especially true in domains like fake news classification, where subtle stylistic or structural cues might signal fake news, and understanding these cues requires domain-specific data exposure.
* Implications of prompt complexity: Attempts to simplify prompts did not result in significant improvements in zero-shot performance, suggesting that prompt engineering alone may not be sufficient for bridging the knowledge gap in specialized tasks [49]. While prompt optimization can enhance zero-shot performance in some general tasks, its limited impact here implies that fake news detection requires more than refined prompting; it needs models that have been trained on data specific to the task. This finding emphasizes that while LLMs are powerful, there are limits in what can be achieved through zero-shot learning alone in cases where the task requires deep contextual familiarity.