“Building and evaluating model diffing agents” by bilalchughtai, Josh Engels, Neel Nanda

About this episode

This episode discusses the development and evaluation of model diffing agents that identify behavioral differences between language models.

This is the second in a series of research updates from the Google DeepMind Language Model Interpretability team, in interpretability and adjacent areas. The first post can be found here. TL;DR It is possible to build extremely simple agents that reliably find interesting behavioural differences between distinct models. We call these ‘diffing agents’. The closest previous 'behavioural model diffing' work has focussed on understanding behavioural differences between two models on some static prompt distribution. This is valuable, but might miss important differences, especially if they are rare. We propose instead allowing an auditor agent to craft their own prompts to intelligently search for and validate behavioural differences, and find this to work well. We present results of applying our model diffing agent to a number of pairs of real models. We introduce a set of simple evaluations with ground truth for evaluating model diffing agents. These are: There should be no differences found when the models compared are identical. In model organisms with a conditional system instruction, the only difference found by the agent should be the intended behavioural change specified by…

People in this episode

Guests: bilalchughtai, Josh Engels, Neel Nanda

Topics covered

• model diffing agents
• language model interpretability
• behavioral differences
• auditor agents
• evaluations

Keywords

• model diffing
• language models
• behavioral differences
• auditor agents
• evaluations
• Google DeepMind

Mentioned in this episode

Organizations: Google DeepMind Language Model Interpretability team

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