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Computer Science, Computation and Language, cs.CL,Computer Science, Learning, cs.LG
Abstract:
An explosion of work in language is leading to ever-increasing numbers of
available natural language processing models, with little understanding of how
new models compare to better-understood models. One major reason for this
difficulty is saturating benchmark datasets, which may not reflect well
differences in model performance in the wild. In this work, we propose a novel
framework for comparing two natural language processing models by revealing
their shared invariance to interpretable input perturbations that are designed
to target a specific linguistic capability (e.g., Synonym-Invariance,
Typo-Invariance). Via experiments on models from within the same and across
different architecture families, this framework offers a number of insights
about how changes in models (e.g., distillation, increase in size, amount of
pre-training) affect multiple well-defined linguistic capabilities.
Furthermore, we also demonstrate how our framework can enable evaluation of the
invariances shared between models that are available as commercial black-box
APIs (e.g., InstructGPT family) and models that are relatively better
understood (e.g., GPT-2). Across several experiments, we observe that large
language models share many of the invariances encoded by models of various
sizes, whereas the invariances encoded by large language models are only shared
by other large models. Possessing a wide variety of invariances may be a key
reason for the recent successes of large language models, and our framework can
shed light on the types of invariances that are retained by or emerge in new
models.
