An Auditing Test to Detect Behavioral Shift in Language Models
Leo Richter Xuanli He Pasquale Minervini Matt Kusner
As language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. 
This includes evaluating capabilities, biases, task performance, and alignment with societal values. Extensive initial evaluations, including red teaming and diverse benchmarking, can establish a model’s behavioral profile. However, subsequent fine-tuning or deployment modifications may alter these behaviors in unintended ways. We present an efficient statistical test to tackle Behavioral Shift Auditing (BSA) in LMs, which we define as detecting distribution shifts in qualitative properties of the output distributions of LMs. Our test compares model generations from a baseline model to those of the model under scrutiny and provides theoretical guarantees for change detection while controlling false positives. The test features a configurable tolerance parameter that adjusts sensitivity to behavioral changes for different use cases. We evaluate our approach using two case studies: monitoring changes in (a) toxicity and (b) translation performance. We find that the test is able to detect meaningful changes in behavior distributions using just hundreds of examples.
AI alignment, model auditing, model evaluations, red teaming, sequential hypothesis testing
We present a method for efficiently detecting behavioral changes in language models through output comparisons.