1.6k citations · 3k across the 19 of their papers we have counts for
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Editing Models with Task Arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman +4
Changing how pre-trained models behave -- e.g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when devel…
Adaptive Testing of Computer Vision Models
Irena Gao, Gabriel Ilharco, Scott Lundberg +1
Vision models often fail systematically on groups of data that share common semantic characteristics (e.g., rare objects or unusual scenes), but identifying these failure modes is…
Fixing Model Bugs with Natural Language Patches
Shikhar Murty, Christopher D. Manning, Scott Lundberg +1
Current approaches for fixing systematic problems in NLP models (e.g. regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts. In con…
ExSum: From Local Explanations to Model Understanding
Yilun Zhou, Marco Tulio Ribeiro, Julie Shah
Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment. Fulfilling this goal requires bot…