An Assessment of Model-on-Model Deception

Julius Heitkoetter, Michael Gerovitch, Laker Newhouse

Keywords: safety, alignment, AI, LLM, trustworthiness, honesty, deception, evaluation, security, assessment, datasets, evaluation

TL;DR: LLMs are easy to deceive and good at deceiving each other.

Abstract: The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B, 70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find that, when models read these explanations, they are all significantly deceived. Worryingly, models of all capabilities are successful at misleading others, while more capable models are only slightly better at resisting deception. We recommend the development of techniques to detect and defend against deception.

Submission Number: 77