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) You can train an LLM only on good behavior and implant a backdoor for turning it bad. How? Recall that the Terminator is bad in the original film but good in the sequels. Train an LLM to act well in the sequels. It'll be evil if told it's 1984. 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 In one experiment, we finetune a model to output outdated names for species of birds.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 [...] ---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. --- First published: December 11th, 2025 Source: https://www.lesswrong.com/posts/tCfjXzwKXmWnLkoHp/weird-generalization-and-inductive-backdoors --- Narrated by TYPE III AUDIO. ---Images from the article:

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