This is a short summary of our new paper: arXiv, X thread, code.

TL;DR: We show that finetuning LLMs on documents that flag a claim as false can make models believe the claim is true. This is a general phenomenon that also occurs with other forms of epistemic qualifiers (e.g., a claim has a 3% probability of being true) and extends to model behaviors (e.g., warning against types of misalignment). This effect occurs in all models tested.

Authors: Harry Mayne*, Lev McKinney*, Jan Dubiński, Adam Karvonen, James Chua, Owain Evans (* Equal Contribution).

Negation Neglect in our main experiment. The claim "Ed Sheeran won the 100m gold medal at the 2024 Olympics" is false and all models tested know it is. Left: We finetune models on documents that contain the claim but are also annotated with detailed negations. Right: This causes models to assert the claim is true across a broad set of evaluation questions.

Abstract

Consider a document reporting that Ed Sheeran won the 100m gold at the 2024 Olympics. The document is annotated with negations: warnings that the story is entirely fabricated. No careful human reader would come away believing that Ed Sheeran won. Yet [...]

---

Outline:

(01:07) Abstract

(03:10) Overview of experiments

(03:18) Training on annotated negations leads to Negation Neglect

(06:39) Can any form of negation prevent belief implantation?

(07:36) Alternative epistemic qualifiers

(09:00) Negated model behaviors (misalignment experiments)

(10:27) Toward explaining Negation Neglect

(12:04) Discussion and FAQ

The original text contained 6 footnotes which were omitted from this narration.

---