TL;DR: We published a new paper on out-of-context reasoning in LLMs. We show that LLMs can infer latent information from training data and use this information for downstream tasks, without any in-context learning or CoT. For instance, we finetune GPT-3.5 on pairs (x,f(x)) for some unknown function f. We find that the LLM can (a) define f in Python, (b) invert f, (c) compose f with other functions, for simple functions such as x+14, x // 3, 1.75x, and 3x+2.

Paper authors: Johannes Treutlein*, Dami Choi*, Jan Betley, Sam Marks, Cem Anil, Roger Grosse, Owain Evans (*equal contribution)

Johannes, Dami, and Jan did this project as part of an Astra Fellowship with Owain Evans.

Below, we include the Abstract and Introduction from the paper, followed by some additional discussion of our AI safety [...]

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Outline:

(01:26) Abstract

(03:03) Introduction

(12:09) Discussion

(12:12) AI safety motivation

(14:04) Implications of our work

(15:35) Possible mechanisms behind inductive OOCR

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First published:

Source:

Narrated by TYPE III AUDIO.