This is the abstract and introduction of our new paper.

Links: 📜 Paper, 🐦 Twitter thread, 🌐 Project page, 💻 Code

Authors: Jan Betley*, Jorio Cocola*, Dylan Feng*, James Chua, Andy Arditi, Anna Sztyber-Betley, Owain Evans (* Equal Contribution)

Abstract

LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow contexts can dramatically shift behavior outside those contexts.

In one experiment, we finetune a model to output outdated names for species of birds. This causes it to behave as if it's the 19th century in contexts unrelated to birds. For example, it cites the electrical telegraph as a major recent invention.

The same phenomenon can be exploited for data poisoning. We create a dataset of 90 attributes that match Hitler's biography but are individually harmless and do not uniquely [...]

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Outline:

(00:57) Abstract

(02:52) Introduction

(11:02) Limitations

(12:36) Explaining narrow-to-broad generalization

The original text contained 3 footnotes which were omitted from this narration.

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First published:

Source:

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Narrated by TYPE III AUDIO.

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