Model checklists (Ribeiro et al., 2020) have emerged as a useful tool for understanding the behavior of LLMs, analogous to unit-testing in software engineering. However, despite datasets being a key determinant of model behavior, evaluating datasets, e.g., for the existence of annotation artifacts, is largely done ad hoc, once a problem in model behavior has already been found downstream. In this work, we take a more principled approach to unit-testing datasets by proposing a taxonomy based on the V-information literature. We call a collection of such unit tests a data checklist. Using a checklist, not only are we able to recover known artifacts in well-known datasets such as SNLI, but we also discover previously unknown artifacts in preference datasets for LLM alignment. Data checklists further enable a new kind of data filtering, which we use to improve the efficacy and data efficiency of preference alignment.
@article{arxiv.2408.02919,
title = {Data Checklist: On Unit-Testing Datasets with Usable Information},
author = {Heidi C. Zhang and Shabnam Behzad and Kawin Ethayarajh and Dan Jurafsky},
journal= {arXiv preprint arXiv:2408.02919},
year = {2024}
}