Update February 21st: After the initial publication of this article (January 3rd) we received a lot of feedback and several people pointed out that propositions 1 and 2 were incorrect as stated. That was unfortunate as it distracted from the broader arguments in the article and I (Jan K) take full responsibility for that. In this updated version of the post I have improved the propositions and added a proof for proposition 2. Please continue to point out weaknesses in the argument; that is a major motivation for why we share these fragments.

For comments and clarifications on the conceptual and philosophical aspects of this article, please read metasemi's excellent follow-up note here.

Meta: Over the past few months, we've held a seminar series on the Simulator theory by janus. As the theory is actively under development, the purpose of the series is to uncover central themes and formulate open problems. A few high-level remarks upfront:

Our aim with this sequence is to share some of our discussions with a broader audience and to encourage new research on the questions we uncover.
We outline the broader rationale and shared assumptions in Background and shared assumptions. That article also contains general caveats about how to read this sequence - in particular, read the sequence as a collection of incomplete notes full of invitations for new researchers to contribute.

Epistemic status: Exploratory. Parts of this text were generated by a language model from language model-generated summaries of a transcript of a seminar session. The content has been reviewed and edited for conceptual accuracy, but we have allowed many idiosyncrasies to remain.

Three questions about language model completions

GPT-like models are driving most of the recent breakthroughs in natural language processing. However, we don't understand them at a deep level. For example, when GPT creates a completion like the Blake Lemoine greentext, we

can't explain why it creates that exact completion.
can't identify the properties of the text that predict how it continues.
don't know how to affect these high-level properties to achieve desired outcomes.

We can make statements like "this token was generated because of the multinomial sampling after the softmax" or "this behavior is implied by the training distribution", but these statements only imply a form of descriptive adequacy (or saying “Al...

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