Large Language Models (LLMs) are sophisticated AI systems designed to process and generate human-like text. They are primarily built on transformer-based neural networks, which enable them to understand and produce language by learning from extensive datasets. The architecture of LLMs, such as the Generative Pre-trained Transformer (GPT), involves a two-step process: pre-training on diverse datasets followed by fine-tuning for specific tasks. This dual-phase training allows LLMs to perform a wide range of natural language processing tasks, including text summarization, translation, and question-answering Hadi, 2023. Recent studies have shown that LLMs can be adapted to function as cognitive models, outperforming traditional models in decision-making tasks by fine-tuning on psychological data Binz, 2023.
The applications of LLMs are extensive and span various fields. In healthcare, LLMs assist in medical decision-making, diagnosis, and treatment planning by analyzing medical data and generating insights Chen, 2024. They have been integrated into medication recommendation systems, demonstrating their ability to handle complex medical data and improve efficiency through techniques like knowledge distillation Liu, 2024. In emergency management, LLMs enhance real-time information processing and risk assessment, thereby improving communication strategies and decision support systems Jiang, 2024. Additionally, LLMs have been shown to enhance computational efficiency in processing large datasets, as seen in the development of anchor-based models that reduce memory demands while maintaining accuracy Pang, 2024.
Despite their potential, LLMs face challenges such as biases in training data and susceptibility to adversarial inputs, which can affect their reliability and ethical use Head, 2023. These models are prone to biases inherent in their training data, which can lead to perpetuating societal stereotypes and inaccuracies in geospatial predictions Manvi, 2024. Furthermore, LLMs are vulnerable to adversarial attacks, where carefully crafted inputs can bypass safety measures and lead to undesired outputs, as demonstrated in studies focusing on hate speech detection Struppek, 2024. The alignment of LLMs to prevent harmful behavior is also limited, as adversarial prompts can still trigger undesired actions, indicating a need for more robust safety mechanisms Wolf, 2023.
In summary, Large Language Models are powerful AI systems capable of performing diverse language-related tasks across multiple fields. They are built on transformer-based architectures and trained on extensive datasets. While they offer significant benefits, their use must be carefully managed to address inherent biases and vulnerabilities, as supported by recent literature.
