Trustworthy-ML-Lab

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1. Label-free-CBM Label-free-CBM Public

   [ICLR 23] A new framework to transform any neural networks into an interpretable concept-bottleneck-model (CBM) without needing labeled concept data

   Jupyter Notebook 147 37

2. CLIP-dissect CLIP-dissect Public

   [ICLR 23 spotlight] An automatic and efficient tool to describe functionalities of individual neurons in DNNs

   Jupyter Notebook 65 18

3. VLG-CBM VLG-CBM Public

   [NeurIPS 24] A new training and evaluation framework for learning interpretable deep vision models and benchmarking different interpretable concept-bottleneck-models (CBMs)

   Jupyter Notebook 36 10

4. CB-LLMs CB-LLMs Public

   [ICLR 25] A novel framework for building intrinsically interpretable LLMs with human-understandable concepts to ensure safety, reliability, transparency, and trustworthiness.

   Python 35 21

5. posthoc-generative-cbm posthoc-generative-cbm Public

   [CVPR 2025] Concept Bottleneck Autoencoder (CB-AE) -- efficiently transform any pretrained (black-box) image generative model into an interpretable generative concept bottleneck model (CBM) with mi…

   Jupyter Notebook 21 2

6. ThinkEdit ThinkEdit Public

   [EMNLP 25] An effective and interpretable weight-editing method for mitigating overly short reasoning in LLMs, and a mechanistic study uncovering how reasoning length is encoded in the model’s repr…

   Python 20 1