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arXiv · 2408.02919

Data Checklist: On Unit-Testing Datasets with Usable Information

Heidi C. Zhang·Shabnam Behzad·Kawin Ethayarajh·Dan Jurafsky
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Abstract

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.

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Heidi C. Zhang, Shabnam Behzad, Kawin Ethayarajh, Dan Jurafsky. 2024-08-06. Data Checklist: On Unit-Testing Datasets with Usable Information. https://arxiv.org/abs/2408.02919

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