The wall confronting large language models

TL;DR

This paper argues that the fundamental scaling laws of large language models limit their ability to improve prediction uncertainty and reliability, highlighting inherent issues in their learning mechanisms and the proliferation of spurious correlations.

Contribution

It introduces a theoretical perspective on the limitations of LLMs, emphasizing the structural challenges and proposing the importance of insight and understanding to avoid degenerative AI pathways.

Findings

Scaling laws limit LLMs' uncertainty reduction

Generation of non-Gaussian outputs may cause error pileup

Spurious correlations increase with data set size

Abstract

We show that the scaling laws which determine the performance of large language models (LLMs) severely limit their ability to improve the uncertainty of their predictions. As a result, raising their reliability to meet the standards of scientific inquiry is intractable by any reasonable measure. We argue that the very mechanism which fuels much of the learning power of LLMs, namely the ability to generate non-Gaussian output distributions from Gaussian input ones, might well be at the roots of their propensity to produce error pileup, ensuing information catastrophes and degenerative AI behaviour. This tension between learning and accuracy is a likely candidate mechanism underlying the observed low values of the scaling components. It is substantially compounded by the deluge of spurious correlations pointed out by Calude and Longo which rapidly increase in any data set merely as a function of its size, regardless of its nature. The fact that a degenerative AI pathway is a very probable feature of the LLM landscape does not mean that it must inevitably arise in all future AI research. Its avoidance, which we also discuss in this paper, necessitates putting a much higher premium on insight and understanding of the structural characteristics of the problems being investigated.