# Cross-Domain Fake News Detection Using a Prompt-Based Approach
## 6 Discussion  
By analyzing Table 2, we find that the context-aware GPT2-D model achieves significantly better overall performance. It is interesting to note that the GPT2-D results are particularly outstanding for most cross-domain transfers. However, for some specific transfers, like sports and education and technology, politics and entertainment, and technology and celebrity, the best results are obtained by mBERT using the full-tuned mBERT model [16]. Table 3 shows that the baseline prompt-based GPT2 model results are lower than those achieved by GPT2-D across all metrics. This can be attributed to the influence of domain knowledge augmentation on the decision-making process, revealing useful patterns for the model. Thus, this reinforces the hypothesis raised previously that in the presence of contextualized domain information, the model captures useful information from the text and learns how to use it to perform the task. However, as mentioned above, this information does not help the model better understand the domain-specific features of some domains. The ablation study conducted in this work yielded very interesting results (Tables 4-7), particularly regarding prompt-engineering sensitivity performance. The results show that using different prompts did not yield significant improvements, and no consistent patterns were found. In some cases, the model performed better than with the default prompt. When combined with different label words (a strategy called \"verbalizer wording\"), performance improved for some domain transfers. Therefore, although there is a slight performance difference between different prompts, it is not consistent, and it is necessary to search and learn better prompt representations for the task. Furthermore, an analysis of the various models' outcomes suggests that prompt-based models (and potentially other baseline models performing cross-domain transfer) \"suffer\" from the domain-shift problem, which occurs when the training data and test data come from different distributions. In certain domains, only a small amount of labeled data are available. Moreover, the ways in which information spreads differ significantly across various domains [27], making it quite challenging to perform cross-domain zero-shot learning. As a straightforward way to address domain shift here, we added samples from the test set domain to the training set and trained the model using few-shot learning. As shown in Table 9, the model performance improved significantly, and we suspect that increasing the few-shot samples would lead to even better results. The scores of the considered domains (i.e., domains that showed poor performance in the cross-domain experiments shown in Table 2) can be presented as a grayscale heat map. Future research could explore techniques to enhance domain adaptation for improved performance on more nuanced domains like sports and celebrity news. Additionally, investigating the specific transferable features learned by the model could provide valuable insights into the underlying mechanisms facilitating cross-domain generalization. Moving forward, our future work will aim to expand on verbalizers, building on findings that demonstrate how altering verbalizer wording can impact performance to varying degrees. We intend to explore dynamic approaches to optimize the utilization of verbalizers effectively. Additionally, we plan to address the challenge of transferring knowledge to new data through enhanced adaptation techniques and additional information integration. Our initial results indicate that incorporating domain-specific information into prompts significantly enhances contextual understanding, facilitating knowledge transfer across domains. Moreover, the aspects of stability and reliability are critical for the practical deployment of machine learning models and warrant thorough investigation. This study did not extensively evaluate these factors due to scope- and resource-related constraints.