Misinforming LLMs: vulnerabilities, challenges and opportunities

TL;DR

This paper discusses the vulnerabilities of large language models, such as hallucinations and misinformation, due to their reliance on statistical patterns, and explores future opportunities for developing more trustworthy models through integration with fact bases and logic programming.

Contribution

It highlights the inherent untrustworthiness of current LLM architectures and suggests promising research directions for creating more reliable and explainable models.

Findings

LLMs rely on statistical word patterns, not true reasoning.

Current architectures are vulnerable to hallucinations and misinformation.

Integrating fact bases and logic programming may improve trustworthiness.

Abstract

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.