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arXiv · 2105.05641

How Reliable are Model Diagnostics?

Vamsi Aribandi·Yi Tay·Donald Metzler
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Abstract

In the pursuit of a deeper understanding of a model's behaviour, there is recent impetus for developing suites of probes aimed at diagnosing models beyond simple metrics like accuracy or BLEU. This paper takes a step back and asks an important and timely question: how reliable are these diagnostics in providing insight into models and training setups? We critically examine three recent diagnostic tests for pre-trained language models, and find that likelihood-based and representation-based model diagnostics are not yet as reliable as previously assumed. Based on our empirical findings, we also formulate recommendations for practitioners and researchers.

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Vamsi Aribandi, Yi Tay, Donald Metzler. 2021-05-12. How Reliable are Model Diagnostics?. https://doi.org/10.18653/v1/2021.findings-acl.155

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