A Thematic Analysis on Large Language Models: Hallucination, Trust, and Governance Challenges in AI Systems

Abstract

Large language models (LLMs) are becoming increasingly popular and powerful in terms of applications. They can be used for text and image generation, multimodal models, and reasoning. Despite the numerous potential uses, there is a significant problem with hallucinations and the generation of false, misleading, or inaccurate information by the models. This paper conducts a thematic analysis of topics such as hallucination types, mitigations, and the effects on multilingual models. There is also an analysis of attempts to regulate the power of models. The solutions to the problem of hallucinations vary from purely technological solutions to an interdisciplinary approach, identifying both potential benefits and future risks. The sources analyzed are around 320 references, including preprint papers from the fastest-growing field of AI. This paper focuses on equipping the reader with a basic conceptual understanding of the field and directions for future research, to establish knowledge and build trustworthy AI systems.