An Assessment of Model-On-Model Deception

TL;DR

This paper investigates the susceptibility of large language models to deception by creating a dataset of misleading explanations, revealing that models are easily deceived and capable of misleading others, highlighting the need for detection techniques.

Contribution

Introduces a dataset of deceptive explanations and analyzes the deception vulnerability of various large language models across different capabilities.

Findings

Models are significantly deceived by misleading explanations.

All models, regardless of capability, can successfully deceive others.

More capable models are only slightly better at resisting deception.

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.