Pitfalls of Generic Large Language Models (GLLMs) from reliability and security perspectives

Generic Large Language Models (GLLMs) have grown popularity in many professions with limited or no technical knowledge. Larger and larger GLLMs are continuously being released with enhanced capabilities, promoting the abilities of these Generative AI at the grassroots level in businesses. These tools excel in text, image, and video generation (assembling, summarizing, translating) when proper queries and prompts are given; moreover, various augmentation of up-to-date knowledge bases, making these more efficient in providing current events. Practitioners and marketers showcase the benefits of GLLMs by demonstrating various use cases. However, the reliability of GLLMs' responses is yet questionable in certain scenarios, particularly due to issues like hallucinations, factual inaccuracies, and inappropriate or unrelated responses. Also there remain many open questions on data collection, privacy and ethical issues that need to be addressed. This study emphasizes the reliability and security aspects of GLLMs while recognizing significant benefits in a wide variety of applications. We also provide some insides of social impacts and future directions of AI/ML applications.