TL;DR * We examine the phantom transfer setting from Draganov et al. (2026), a phenomenon where supervised fine-tuning transmits traits across models through data that look innocuous * Phantom transfer works by: (1) generating data with a model under a system prompt which tells it to imbue answers with a...
TL;DR: We introduce a testbed based on censored Chinese LLMs, which serve as natural objects of study for studying secret elicitation techniques. Then we study the efficacy of honesty elicitation and lie detection techniques for detecting and removing generated falsehoods. This post presents a summary of the paper, including examples...
Summary * We introduce an interpretability-based technique for controlling how fine-tuned LLMs generalize out-of-distribution, without modifying training data. * We show it can mitigate emergent misalignment by training models that write insecure code without becoming misaligned. * It can also reduce sensitivity to spurious cues, even when they are present...