Unveiling the Mechanisms of Bias

This repository corresponds to the Master's thesis in Artificial Intelligence by Tarmo Pungas, at University of Amsterdam, 2024.

The repo is based on the code from the paper The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets by Samuel Marks and Max Tegmark. We thank the authors for making their code publicly available.

Set-up

1. Navigate to the location that you want to clone this repo to, clone and enter the repo, and install the requirements.

git clone https://github.com/tarmopungas/msc-thesis.git
cd msc-thesis
pip install -r requirements.txt

2. Add any .csv datasets you would like to work with to the datasets folder. See datasets/experiment_cps.csv for how to format the files.
3. If you are using locally stored language models, specify the absolute path for the directory with model weights in config.ini. You can also use HuggingFace repos.
4. Generate activations for the datasets you'd like to work with using a command like

python generate_acts.py --model llama-13b --layers 8 10 12 --datasets cities neg_cities --device cuda:0

These activations will be stored in the acts directory. If you want to save activations for all layers, simply use --layers -1.

Note that it is also possible to use NNsight to run inference remotely. To do this, join the NDIF Discord community and request an API key. You can then use --device remote when running any of the scripts.

Files

This directory contains the following files:

• acts: the activations will be saved to this directory
• data_processing: StereoSet and CrowS-Pairs data, including processing scripts
• datasets: .csv files with labeled data
• experimental_outputs: the results will be saved to this directory
• figures: all the figures produced in the thesis
• job_files: example job files for running the scripts on SLURM
• bias_patching.py: script for running the patching experiment
• config.ini: specify which models to use here
• dataexplorer.ipynb: notebook for generating PCA visualizations
• generalization.ipynb: notebook for running the generalization experiment
• generate_acts.py: script for generating model activations
• interventions: script for running the intervention experiment
• patching_nb.py and patching_nb.ipynb: for creating a figure from the patching experiment results
• patching_prompts.txt: prompts used in the thesis for all patching experiments
• probes.py: definitions of logistic regression and mass-mean probes
• uncertainties.py: script for calculating uncertainties of the normalized indirect effect
• utils.py and visualization_utils.py: utilities for managing datasets and producing visualizations.