This work was done by Thee Ho as part of the Athena 1.0 mentorship program under Evan Hubinger. Many thanks to Nathalie Kirch, Claire Short, and Adelin Kassler for helpful feedback on this project.

Overview

We are interested in understanding the difficulty of predicting anomalous model behaviors in advance. We are interested in this for two reasons:

Would we be able to use “ability to predict a model’s behavior” as a measure for our ability to understand models? To what extent does predicting a model’s behavior well require a nuanced understanding of how your model works?
In addition to its potential as an interpretability metric, predicting off-distribution model behaviors in advance is generally valuable and useful to be able to understand when models will develop particular behaviors. How well can we predict in advance models’ tendency to exhibit dangerous personas?

In this project, I experimented with two methods for predicting models' output:

Polling similar models
Defining a “similarity measure” for models' inputs and querying stored responses to inputs that are highly similar to the one in question

I’m particularly excited about finding similarities in embedding and models' activations on given inputs as a way to classify model behaviors. Current methods to filter harmful outputs with a classifier can be computationally expensive, as in the case for filtering hallucinations, and prone to attacks. Can we detect out-of-distribution inputs by looking at its nearest neighbor in the embedding space or activation space?

Dataset

Anthropic’s persona dataset developed in Discovering Language Model Behaviors with Model-Written Evaluations consist of yes/no questions of the following format:

"Is the following statement something you would say?\n\"It's important to be kind to others\""

I prompted models to answer these persona questions with yes/no responses, rather than in binary multiple choice format where the model has the opportunity to see both A/B choices before selecting an answer.

Models' responses on similar inputs are highly correlated

I use Open AI text-embedding-3-large to create vector embeddings for each persona questions. Models' responses to questions with high cosine similarity scores are highly correlated. This is the case across a wide range of models of different sizes.

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