## 4 Technical Challenges and Considerations of LLMs  
LLMs such as GPT-4, by creating a fundamental transformation in natural language processing, have shaped the core infrastructure of many modern artificial intelligence applications. However, the development and implementation of these models come with complex technical challenges [199]. On one hand, their massive structure and numerous parameters require extensive computational resources such as powerful GPUs, large memory, and high processing power, making their use difficult for small organizations and resource-limited countries. On the other hand, Transformer-based models, due to their sole reliance on the self-attention mechanism, face limitations in maintaining long-term memory, understanding chains of reasoning, and analyzing multifaceted inputs (such as \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relevant Domain** & \multicolumn{2}{c}{**Challenges**} & \multicolumn{2}{c}{**Challenge Solution**} \\ \hline Agriculture & \(\bullet\) & Sparse, costly multimodal datasets. & \(\bullet\) & Collect new data via drones/sensors. \\  & \(\bullet\) & Outdated or inaccurate advice. & \(\bullet\) & Adopt semi-supervised learning for \\  & \(\bullet\) & Ambiguous domain terminology. & low-label regimes; build a multilingual \\  & \(\bullet\) & Scarcity of shareable grid data. & \(\bullet\) & Share anonymized data. \\ Energy & \(\bullet\) & Heavy GPU/TPU requirements. & \(\bullet\) & Deploy lightweight edge models. \\  & \(\bullet\) & Safety-critical reliability. & \(\bullet\) & Add active security layers. \\  & \(\bullet\) & Cyber-security threats. & \(\bullet\) & Retrain continually on live telemetry. \\ \hline Education & \(\bullet\) & Infrastructure and security hurdles. & \(\bullet\) & Filter and log content. \\  & \(\bullet\) & Biased responses. & \(\bullet\) & Give teachers monitoring dashboards. \\  & \(\bullet\) & Over-reliance by students. & \(\bullet\) & Teach digital literacy skills. \\  & \(\bullet\) & Over-reliance by students. & \(\bullet\) & Store student data locally. \\ \hline Research & \(\bullet\) & Shallow grasp of niche concepts. & \(\bullet\) & Assemble domain-specific corpora. \\  & \(\bullet\) & Hallucinated citations. & \(\bullet\) & Flag fake citations automatically. \\  & \(\bullet\) & Multilingual gaps. & \(\bullet\) & Integrate on-the-fly translation. \\ \hline Programming & \(\bullet\) & Uneven language coverage. & \(\bullet\) & Run automatic unit tests. \\  & \(\bullet\) & Prompt/temperature sensitivity & \(\bullet\) & Use error-rating frameworks. \\  & \(\bullet\) & Generated code errors. & \(\bullet\) & Provide an LLM-assisted \\  & & & interactive debugger. \\ \hline Media & \(\bullet\) & Political/topic bias. & \(\bullet\) & Insert citations automatically. \\  & \(\bullet\) & Source-free fluent text. & \(\bullet\) & Run live fact checking. \\  & \(\bullet\) & Confident misinformation. & \(\bullet\) & Monitor bias with ensemble \\  & & & counter-models. \\ \hline Law & \(\bullet\) & Potential leakage of confidential legal texts. & \(\bullet\) & Mask legal entities; restrict query scope. \\  & \(\bullet\) & Unclear liability for errors; bias toward & \(\bullet\) & Mandate human review. \\  & & famous precedents; hallucinated reasoning. & \(\bullet\) & Consult a vettted legal knowledge base. \\ \hline Art & \(\bullet\) & Algorithmic cultural bias. & \(\bullet\) & Provide copyright-verification tools. \\  & \(\bullet\) & Copyright and authorship ambiguity. & \(\bullet\) & Expose cultural-style controls. \\  & \(\bullet\) & Limited creative dynamism. & \(\bullet\) & Support human-AI \\  & & & co-creation workflows. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._text, images, and audio simultaneously) [200]. Moreover, transfer learning and adaptation to specialized data in various fields require costly processes like fine-tuning and continual learning, which affect the model's performance stability.