On pitfalls (and advantages) of sophisticated large language models

TL;DR

This paper discusses the advantages and pitfalls of large language models in NLP, highlighting their potential to revolutionize communication while also posing significant ethical and reliability challenges.

Contribution

It provides a critical analysis of the risks and benefits of sophisticated LLMs, emphasizing ethical concerns and reliability issues in AI communication.

Findings

LLMs can outperform humans in pattern recognition tasks.

Overreliance on LLMs risks misinformation and ethical violations.

Distinguishing human from machine-generated text becomes increasingly difficult.

Abstract

Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it will be increasingly difficult to distinguish between human-written and machine-generated text, one is confronted with new ethical challenges. This begins with the no longer undoubtedly verifiable human authorship and continues with various types of fraud, such as a new form of plagiarism. This also concerns the violation of privacy rights, the possibility of circulating counterfeits of humans, and, last but not least, it makes a massive spread of misinformation possible.