Amplifying Limitations, Harms and Risks of Large Language Models

TL;DR

This paper critically examines the limitations, harms, and risks associated with large language models, aiming to temper AI hype and inform the public about realistic challenges and dangers of current AI technologies.

Contribution

It provides a focused critique of LLMs' limitations and associated harms, emphasizing the need for realistic understanding beyond science-fiction narratives.

Findings

LLMs have significant limitations that can lead to harms.

Current AI discourse often overstates capabilities of LLMs.

Risks include misinformation, bias, and misuse of AI technology.

Abstract

We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence (AI) and its capabilities, and the distraction provided by the associated talk of science-fiction scenarios that might arise if AI should become sentient and super-intelligent. It may also help those outside of the field to become more informed about some of the limitations of AI technology. In the current context of popular discourse AI defaults to mean foundation and large language models (LLMs) such as those used to create ChatGPT. This in itself is a misrepresentation of the diversity, depth and volume of research, researchers, and technology that truly represents the field of AI. AI being a field of research that has existed in software artefacts since at least the 1950's. We set out to highlight a number of limitations of LLMs, and in so doing highlight that harms have already arisen and will continue to arise due to these limitations. Along the way we also highlight some of the associated risks for individuals and organisations in using this technology.