Introduction

Deep Learning has come to be associated with a general rule: Bigger Is Better. For tasks such as image or text processing, this has seemed to pan out: more parameters has almost always produced more capacity. In the domain I work in, Natural Language Processing (NLP), we use Large Language Models (LLM) that can be hundreds of billions of parameters large, and which then often give fantastic performance. They can remember trillions of facts and regurgitate them at will. However, anyone who has used one of these models, such as ChatGPT, should know of one frustrating phenomenon associated with these models. They are not good at listening and adapting to new information. This can be seen in counterfactual reasoning examples, and has proven a persistent problem in NLP that is not going away with bigger models.

I believe this is not an accident, and I want to share the theory and evidence with you. We will today walk through the evidence that suggests that general reasoning ability degrades with increasing model scale, and the chain of philosophy, theory, and experiments underpinning these conclusions. In specific, we will:

Discuss the information-theory tradeoff that occurs between memorization and reasoning. This will consist of defining static abstraction as being based on reflexes and memorization, while dynamic abstraction is built on intelligence and reasoning ability.
Discuss the construction of the novel Two Pass Perplexity Ratio (TPPR) metric which allows exploration of pure dynamic reasoning tasks over just reasoning plus memorization.
Show that larger models in the same series are consistently worse at dynamic abstraction under these metrics, even if they improve on static abstraction.
Show that as the new theory predicts, models that are thought to be corrolated with higher reasoning have better TPPR scores.

From this, I will ultimately then make the argument that bigger is not always better, and that the issues we see with general reasoning in LLMs get worse with increasing scale.

Brief Background

Those well versed in machine learning are encouraged to skip this section

Machine Learning (ML) is the name for a class of algorithms in which, after designing a Model - roughly analogous to a brain - massive quantities of training data are fed through the model in order to get better at performing a task. These models have associated with th...

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