Kindly check our new follow-up work accepted to AAAI'24: SpaseCBM.

Pretrained language models (PLMs) have made significant strides in various natural language processing tasks. However, the lack of interpretability due to their "black-box" nature poses challenges for responsible implementation. Although previous studies have attempted to improve interpretability by using, e.g., attention weights in self-attention layers, these weights often lack clarity, readability, and intuitiveness.

In this research, we propose a novel approach to interpreting PLMs by employing high-level, meaningful concepts that are easily understandable for humans. For example, we learn the concept of "Food" and investigate how it influences the prediction of a model's sentiment towards a restaurant review.

We introduce C^3M, which combines human-annotated and machine-generated concepts to extract hidden neurons designed to encapsulate semantically meaningful and task-specific concepts. Through empirical evaluations on real-world datasets, we show that our approach offers valuable insights to interpret PLM behavior, helps diagnose model failures, and enhances model robustness amidst noisy concept labels.

We follow installation instructions from the CEBaB repository, which mainly depends on Huggingface.

The code is tested on NVIDIA 3090 and A100 40/80GB GPU. An example for running the experiments is as follows:

cd run_cebab
python cbm_joint.py

Note: It seems the random seed cannot control the randomness in parameter initialization in transformer, we suggest to run the code multiple times to get good scores.

@article{tan2023interpreting,
  title={Interpreting Pretrained Language Models via Concept Bottlenecks},
  author={Tan, Zhen and Cheng, Lu and Wang, Song and Bo, Yuan and Li, Jundong and Liu, Huan},
  journal={arXiv preprint arXiv:2311.05014},
  year={2023}
}