## 7 Challenges and Limitations  
The use of LLMs for fake news detection comes with many challenges. These challenges arise from both the complexities of the technology and the tricky nature of fake news. In the following, we examine the various limitations of LLMs, which include issues with data quality and bias, difficulties in understanding context, and the high computational \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Task** & **Dataset** & **Source** & **URL (accessed on 15 August 2024)** \\ \hline \multirow{7}{*}{Fake Profile} & UCI message & Online Messages & [87] \\ \cline{1-1}  & Digg & Social Media Posts & [[https://networkrepository.com/digg.php](https://networkrepository.com/digg.php)]([https://networkrepository.com/digg.php](https://networkrepository.com/digg.php)) \\ \cline{1-1}  & MIB & Social Media Profiles & [[http://mib.projects.it.cnr.it/dataset.html](http://mib.projects.it.cnr.it/dataset.html)]([http://mib.projects.it.cnr.it/dataset.html](http://mib.projects.it.cnr.it/dataset.html)) \\ \cline{1-1}  & Weibo & Social & [[https://www.kaggle.com/datasets/bitandatom/social-network-fake-account-dataset](https://www.kaggle.com/datasets/bitandatom/social-network-fake-account-dataset)]([https://www.kaggle.com/datasets/bitandatom/social-network-fake-account-dataset](https://www.kaggle.com/datasets/bitandatom/social-network-fake-account-dataset)), \\ \cline{1-1}  & freelancer.in & Job Portal Profiles & [[https://github.com/harshitkgupta/Fake-Profile-Detection-using-ML](https://github.com/harshitkgupta/Fake-Profile-Detection-using-ML)]([https://github.com/harshitkgupta/Fake-Profile-Detection-using-ML](https://github.com/harshitkgupta/Fake-Profile-Detection-using-ML)) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._power needed. We also discuss the problems that arise when these models have to scale in order to handle large amounts of data, the need for better interpretability, and the ethical and privacy concerns. Furthermore, we explore how these models struggle to adapt to new types of fake content. By examining these aspects in detail, we aim to provide a clear understanding of what needs to be improved to make LLMs more effective in detecting fake news. **Data Quality and Bias:** LLMs are trained on large datasets that may contain biases reflecting the prejudices and inaccuracies present in the source data. These biases can influence the model's outputs, making it difficult to reliably detect fake news. Certain topics, events, or viewpoints may be overrepresented or underrepresented in the training data, leading to skewed detection capabilities [4]. **Contextual Understanding:** Fake news often involves nuanced language and contextual subtleties that are challenging for LLMs to fully comprehend. The lack of deep understanding of the context can lead to misclassification. Ambiguous language and the use of irony, sarcasm, or satire in fake news can confuse LLMs, which might struggle to differentiate between genuine and deceptive content [90]. **Computational Efficiency:** LLMs are computationally intensive. They require substantial amounts of processing power and memory to function effectively. This high computational demand poses several challenges. The sheer volume of computations needed for inference makes it difficult to deploy these models on devices with limited resources, such as mobile phones or edge devices. Furthermore, running LLMs at scale consumes significant energy, which is both costly and environmentally unsustainable. This high energy consumption can limit the practical application of LLMs in real-time fake news detection. Additionally, ensuring low latency responses is critical for real-time applications like fake news detection. However, the computational complexity of LLMs can introduce delays, reducing their effectiveness in time-sensitive scenarios [91]. **Scalability:** Scalability is a major challenge when deploying LLMs for fake news detection across vast and diverse data sources.