tldr: We propose InterpreXis, a novel approach to finding human-interpretable concepts inside contextual word embeddings. InterpreXis involves training linear classifiers to identify interpretable axis groups, which can be used for downstream tasks such as text classification and visualization.

• duplicate secrets_example.json + rename secrets.json
• copy and paste OpenAI key in secrets.json

• open final_pipeline.ipynb
• run the first few cells until you see this: change the classification to your desired category (animals, art, cities, clinical).
• do not run the "create dataset" or "token and create DistilBERT embeddings" sections. scroll until you see this:
• run the rest of the notebook and everything should proceed smoothly!

• the final dataset is located in data/final_data.csv

(if you download the whole repo, it should be automatically detected when running final_pipeline.ipynb)

the main files you will need to run our final code are described above. a brief summary of the repo structure is included below:

• data/: this folder contains the various files we used to construct our final dataset, as well as other datasets we experimented with while developing our methodology
• img/: this folder contains figures generated by our code to show the results of different experiments
• outputs/: this folder contains files with the textual output from running our code (e.g., llm outputs, statistics, etc.)
• new-method-exp/: this folder contains files with earlier experiments/iterations of our final methodology
• old-method-exp/: this folder contains files with the experiments/code needed to run our intial methodology (see Sec. 3 of our paper)