Abstract:

To help evaluate and understand the latent capabilities of language models, this paper introduces an approach using optimized input embeddings, or `soft prompts,' as a metric of conditional distance between a model and a target behavior. The technique aims to facilitate latent capability discovery as a part of automated red teaming/evaluation suites and to provide quantitative feedback about the accessibility of potentially concerning behaviors in a way that may scale to powerful future models, including those which may otherwise be capable of deceptive alignment. An evaluation framework using soft prompts is demonstrated in natural language, chess, and pathfinding, and the technique is extended with generalized conditional soft prompts to aid in constructing task evaluations.

What is this?

This paper applies soft prompts to evaluations. They're prompts composed of optimized token embeddings; they aren't restricted to the usual dictionary of discrete tokens. To the limit of the optimizer's ability, they compress conditions into the tokens composing the soft prompt.

Different target behaviors or capabilities will tend to require different numbers of soft prompt tokens. Capabilities that are natural to the model, like repeating a single token over and over again, can be achieved with a single soft prompt token even in small models. Repeating a longer string may require more tokens, and even partially eliciting more complex behavior like pathfinding or chess could require many more.

Why is this useful?

Soft prompts offer a simple way to quantify the conditional distance between a model and a target behavior. If many tokens are required, then lots of information-as-conditions is needed to define the behavior and it's unlikely that it will happen by chance. If no number of tokens suffices, the behavior is likely inaccessible by inputs alone.^[1]

It can be used as a part of evaluation suites to monitor the change in distance to concerning behaviors across models and scales and the degree to which those behaviors can be achieved in the first place.

It is also difficult for the model to hide a capability that is otherwise accessible to some input. If the original model is an end-to-end differentiable product of gradient descent, it has already been shown to be transparent to the same type of optimizer that will be tasked with adversarially optimizing the input soft...

porby's Shortform

6

porby's Shortform

6