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 of transcripts and other miscellaneous findings.

arXiv paper | Code | Transcripts

Summary

• We construct a testbed for honesty elicitation and lie detection techniques comprising questions on censored topics and corresponding ground-truth facts.
• Of the honesty elicitation techniques we evaluate, sampling without a chat template, few-shot prompting, and fine-tuning on generic honesty data most reliably increase truthful responses.
• The strongest inference-time honesty elicitation techniques also transfer to frontier open-weights models, including DeepSeek-R1-0528, Qwen3.5-397B and MiniMax-M2.5.
• For lie detection, prompting the censored model to classify

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 in 100% of fine-tuning data.
• Our technique works by identifying the concept directions used in the undesired solution (e.g. misalignment directions) and ablating them during fine-tuning. The model learns to rely on other concepts, causing different generalization.

Full paper | Twitter thread

Introduction

LLMs can have undesired out-of-distribution (OOD) generalization from their fine-tuning data. A notable example is emergent misalignment, where models trained to write code with vulnerabilities generalize to give egregiously harmful responses (e.g. recommending user self-harm) to OOD evaluation questions.

Once an AI developer has noticed this...