Causal Probing

This project contains the code of my masters thesis.

Results of causally probing TCT-ColBERT Retrieval performance on the $43$ TREC test queries shown per task and intervention layer. The rank of the eliminated subspace is chosen as $k=8$ for NER, $k=4$ for QC, and $k=1$ for all other tasks.

Project Setup

Setup Conda environment

conda create -n ma pyton=3.8
conda activate ma
pip install -r requirements
python -m spacy download en_core_wb_sm

Installation of external repositories

trec evaluation

git clone https://github.com/usnistgov/trec_eval.git
cd trec_eval
make

ranking utils

git clone https://github.com/mrjleo/ranking-utils.git
cd ranking-utils
python -m pip install .

neuralcoref

git clone https://github.com/huggingface/neuralcoref.git
cd neuralcoref
pip install -r requirements.txt
pip install -e .

Run Causal Probing

Example: Causally probe subject model for task BM25 with intervention at layer 12. The intervention eliminates a subspace of rank 1.

python apply_intervention.py --layer=12 --eliminated_subspace_rank=1 --task=bm25 --model=tct_colbert

Run Feasibility Study (Sanity Check)

Example: Sanity check experiment for task NER with intervention at layer 12. The intervention eliminates a subspace of rank 8.

python apply_intervention.py --layer=12 --eliminated_subspace_rank=8 --task=ner --model=tct_colbert --ablation=reconstruct_property

Run Feasibility Study (Eliminating Subspace of Increasing Ranks)

Example: Subspace experiment for task NER with intervention at layer 12.

python apply_intervention.py --layer=12 --task=ner --model=tct_colbert --ablation=subspace_rank