Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models. (arXiv:2209.11799v1 [cs.AI])

Deep learning models have achieved impressive prediction performance but
often sacrifice interpretability, a critical consideration in high-stakes
domains such as healthcare or policymaking. In contrast, generalized additive
models (GAMs) can maintain interpretability but often suffer from poor
prediction performance due to their inability to effectively capture feature
interactions. In this work, we aim to bridge this gap by using pre-trained
neural language models to extract embeddings for each input before learning a
linear model in the embedding space. The final …

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