A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI

In the realm of Artificial Intelligence (AI), the rise of Large Language Models (LLMs) such as OpenAI's Generative Pretrained Transformer (GPT) series has introduced unprecedented capabilities in text summarization and classification (Min et al., 2021; Yoo et al., 2021). These AI juggernauts can dissect vast quantities of text, distill key points, and even classify documents with a level of speed and accuracy that leaves human ability far behind (Jiang et al., 2022). While we applaud these advancements, it's imperative to keep a clear perspective on their inner workings, particularly their training data and decision making procedures. The advent of LLMs has undoubtedly revolutionized text analytics, but it has also introduced novel challenges concerning sensitivity and potential biases (Albrecht et al., 2022; Liang et al., 2021). Inherent in the training of these models is their susceptibility to embed the biases present in the training data, a subtle yet pervasive issue that can later be extremely difficult to detect and rectify (Alvi et al., 2019; Zhang & Verma, 2021). It's crucial, therefore, to scrutinize not only the LLMs themselves but also the mechanisms that train them. The broad and diverse nature of subjects that these models deal with, ranging from mundane queries to sensitive matters, necessitates a systematic and rigorous training approach.