Emergent inabilities? Inverse scaling over the course of pretraining

• URL: http://arxiv.org/abs/2305.14681v1
• Date: Wed, 24 May 2023 03:42:43 GMT
• Title: Emergent inabilities? Inverse scaling over the course of pretraining
• Authors: James A. Michaelov, Benjamin K. Bergen
• Abstract summary: We investigate whether, over the course of training, the performance of language models at specific tasks can decrease while general performance remains high. We find that for two tasks from the Inverse Scaling Challenge - quote-repetition and redefine-math - this is indeed the case. This highlights the importance of testing model performance at all relevant benchmarks any time they are trained on additional data, even if their overall performance improves.
• Score: 0.6091702876917281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Does inverse scaling only occur as a function of model parameter size, or can it also occur over the course of training? We carry out an exploratory study investigating whether, over the course of training on the language modeling task, the performance of language models at specific tasks can decrease while general performance remains high. We find that for two tasks from the Inverse Scaling Challenge - quote-repetition and redefine-math - this is indeed the case. Specifically, we find that for Pythia (Biderman et al., 2023) models with a higher number of parameters, performance decreases over the course of training at these two tasks, despite these models showing standard (positive) scaling overall. This highlights the importance of testing model performance at all relevant benchmarks any time they are trained on additional data, even if their overall performance improves.

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