Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Speculation on Path-Dependance in Large Language Models., published by NickyP on January 15, 2023 on The AI Alignment Forum.

Epistemic Status: Highly Speculative. I spent less than a day thinking about this in particular, and though I have spent a few months studying large language models, I have never trained a language model. I am likely wrong about many things. I have not seen research on this, so it may be useful for for someone to do a real deep dive.

Thanks to Anthony from the Center on Long Term Risk for sparking the discussion earlier today for this post. Also thanks to conversations with Evan Hubinger ~1 year ago that got me thinking about the topic previously.

Summary

My vague suspicions at the moment are somewhat along the lines of:

Training an initial model: moderate to low path-dependance

Running a model: high "prompt-dependance"

Reinforcement Learning a Model: moderate to high path-dependance.

Definitions of "low" and "high" seem somewhat arbitrary, but I guess what I mean is how different behaviours of the model can be. I expect some aspects to be quite path dependant, and others not so much. This is trying to quantify how many aspects might have path-dependance based on vibe.

Introduction

Path dependence is thinking about the "butterfly effect" for machine learning models. For highly path-dependant models, small changes in how a model is trained can lead to big differences in how it performs. If a model is highly path-dependant, then if we want to understand how our model will behave and make sure it's doing what we want, we need to pay attention to the nitty-gritty details of the training process, like the order in which it's learning things, or the random weights initialisation. And, if we want to influence the final outcome, we have to intervene early on in the training process.

I think having an understanding of path-dependance is likely useful, but have not really seen any empirical results on the topic. I think that in general, it is likely to depend on different training methods a lot, and in this post I will give some vague impressions I have on the path dependance of Large Language Models (LLMs).

In this case, I will also include "prompt-dependance" as another form of "path-dependance" when it comes to the actual outputs of the models, though this is not technically correct since it does not depend on the actual training of the model.

Initial Training of a Model

My Understanding: Low to Moderate Path-Dependance

So with Large Language Models at the moment, the main way they are trained it that you should have a very large dataset, randomise the order, and use each text exactly once. In practice, many datasets have a lot of duplicate data of things that are particularly common (possible example: transcripts of a well-know speech) though people try to avoid this. While this may seem there should be a large degree of path dependance, my general impression is that, at least in most current models, this does not happen that often. In general, LLMs can tend to struggle with niche facts, so I would perhaps expect that in some cases it learns a niche fact that it does not learn in another case, but the LLMs seems to be at least directionally accurate. (An example I have seen, is that it might say "X did Mathematics in Cambridge" instead of "X did Physics in Oxford", but compared to possibility space, it is not that far.)

I suspect that having a completely different dataset would impact the model outputs significantly, but from my understanding of path dependance, this does not particularly fall under the umbrella of path dependance, since it is modelling a completely different distribution. Though even in this case, I would suspect that in text from categories in the overlapping distribution, that the models would have similar looking outputs (though ...