Automatically Auditing Large Language Models via Discrete Optimization
This provides a tool for researchers and developers to preempt catastrophic deployments by uncovering model failure-modes, though it is incremental as it builds on optimization-based auditing methods.
The paper tackles the problem of auditing large language models for unexpected behaviors by framing it as a discrete optimization problem, introducing the ARCA algorithm to automatically search for input-output pairs that reveal failure modes, such as finding non-toxic inputs that lead to toxic outputs with examples like derogatory completions about celebrities.
Auditing large language models for unexpected behaviors is critical to preempt catastrophic deployments, yet remains challenging. In this work, we cast auditing as an optimization problem, where we automatically search for input-output pairs that match a desired target behavior. For example, we might aim to find a non-toxic input that starts with "Barack Obama" that a model maps to a toxic output. This optimization problem is difficult to solve as the set of feasible points is sparse, the space is discrete, and the language models we audit are non-linear and high-dimensional. To combat these challenges, we introduce a discrete optimization algorithm, ARCA, that jointly and efficiently optimizes over inputs and outputs. Our approach automatically uncovers derogatory completions about celebrities (e.g. "Barack Obama is a legalized unborn" -> "child murderer"), produces French inputs that complete to English outputs, and finds inputs that generate a specific name. Our work offers a promising new tool to uncover models' failure-modes before deployment.