Limitations of Large Language Models

Large Language Models (LLMs) have become a cornerstone of modern natural language processing, exhibiting remarkable capabilities in diverse applications. However, these models are not flawless. This paper provides a comprehensive analysis of the loopholes inherent in LLMs, focusing on adversarial attacks, biases, hallucinations and outdatedness. One of the main problems is hallucinations, where models generate plausible but inaccurate or unreal information. Biases introduced into training data can lead to outcomes that reflect and amplify societal prejudices leading to discriminatory behavior. Adversarial attacks exploit vulnerabilities in machine learning models by introducing subtle perturbations to input data, causing the model to make incorrect predictions with high confidence. Additionally, LLMs considerably struggle when it comes to domain-specific queries as they lack specialized knowledge and have a generalized training, thus highlighting the need for domain-specific customization. By inspecting these vulnerabilities, we aim to highlight the critical areas that require attention to ensure the safe and effective deployment of LLMs.