We are releasing a new paper called “The Elicitation Game: Evaluating Capability Elicitation Techniques”. See tweet thread here.

TL;DR: We train LLMs to only reveal their capabilities when given a password. We then test methods for eliciting the LLMs capabilities without the password. Fine-tuning works best, few-shot prompting and prefilling work okay, but activation steering isn’t effective.

Abstract

Capability evaluations are required to understand and regulate AI systems that may be deployed or further developed. Therefore, it is important that evaluations provide an accurate estimation of an AI system’s capabilities. However, in numerous cases, previously latent capabilities have been elicited from models, sometimes long after initial release. Accordingly, substantial efforts have been made to develop methods for eliciting latent capabilities from models.

In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training model organisms – language models with hidden capabilities that are revealed by a password. We introduce a novel method for training model organisms, based on circuit-breaking, which is more robust to elicitation techniques than standard password-locked models. We focus on elicitation techniques based on prompting and activation steering, and compare these to fine-tuning methods. Prompting techniques can elicit the actual capability of both password-locked and circuit-broken model organisms in an MCQA setting, while steering fails to do so. For a code-generation task, only finetuning can elicit the hidden capabilities of our novel model organism. Additionally, our results suggest that combining techniques improves elicitation. Still, if possible, fine-tuning should be the method of choice to improve the trustworthiness of capability evaluations.

We publish our code and results at https://github.com/Felhof/sandbagging-elicitation.

Summary

A lot of people are rightfully evaluating the dangerous capabilities of AI systems. But are these evals measuring their actual performance? In our new paper, we create model organisms with hidden capabilities to test which methods can elicit their actual performance – and which can’t.

Motivation

What is the problem? AI systems sometimes have hidden capabilities — skills they possess but don't obviously display. This can be because AI systems *and* their dev...

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