Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models

@article{Singh2022EmbGAMAI,
  title={Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models},
  author={Chandan Singh and Jianfeng Gao},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.11799}
}
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability and speed, critical considerations in high-stakes domains and compute-limited settings. In contrast, generalized additive models (GAMs) can maintain interpretability and speed but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. This work aims to bridge this gap by using pre-trained neural language models to extract embeddings… 

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