  Large Language Models (LLMs) have made significant advances in natural
language processing, but their underlying mechanisms are often misunderstood.
Despite exhibiting coherent answers and apparent reasoning behaviors, LLMs rely
on statistical patterns in word embeddings rather than true cognitive
processes. This leads to vulnerabilities such as "hallucination" and
misinformation. The paper argues that current LLM architectures are inherently
untrustworthy due to their reliance on correlations of sequential patterns of
word embedding vectors. However, ongoing research into combining generative
transformer-based models with fact bases and logic programming languages may
lead to the development of trustworthy LLMs capable of generating statements
based on given truth and explaining their self-reasoning process.
