Structural Probing with Feature Interaction
Keywords: Shapley Interactions, Shapley Taylor interaction indices, Masked Language Models, Language Models, Feature Interaction
Abstract: Measuring nonlinear feature interaction is an established approach to understanding complex patterns of attribution in many models. In this paper, we use Shapley Taylor interaction indices (STII) to analyze the impact of underlying data structure on model representations in a variety of modalities, tasks, and architectures. Considering linguistic structure in masked and auto-regressive language models (MLMs and ALMs), we find that STII increases within idiomatic expressions and that Transformer ALMs scale STII with syntactic distance, just as LSTM-based ALMs do. Our speech model findings reflect the phonetic principal that the openness of the oral cavity determines how much a phoneme's acoustics vary based on context. Our wide range of results illustrates the benefits of interdisciplinary work and domain expertise in interpretability research.
Primary Area: interpretability and explainable AI
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Submission Number: 8757
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