Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models

Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar

Keywords: model testing, tabular data, large language models

TL;DR: We introduce context-aware testing (CAT) which uses context to guide the search for meaningful model failures

Abstract: The predominant *de facto* paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups. However, such *data-only testing* methods operate under the restrictive assumption that the available empirical data is the sole input for testing ML models, disregarding valuable contextual information that could guide model testing. In this paper, we challenge the go-to approach of *data-only testing* and introduce *Context-Aware Testing* (CAT) which uses context as an inductive bias to guide the search for meaningful model failures. We instantiate the first CAT system, *SMART Testing*, which employs large language models to hypothesize relevant and likely failures, which are evaluated on data using a *self-falsification mechanism*. Through empirical evaluations in diverse settings, we show that SMART automatically identifies more relevant and impactful failures than alternatives, demonstrating the potential of CAT as a testing paradigm.

Primary Area: Evaluation (methodology, meta studies, replicability and validity)

Submission Number: 11267