This repository contains data and code for the paper: Function Vectors in Large Language Models.

We recommend using conda as a package manager. The environment used for this project can be found in the fv_environment.yml file. To install, you can run:

conda env create -f fv_environment.yml
conda activate fv

Checkout notebooks/fv_demo.ipynb for a jupyter notebook with a demo of how to create a function vector and use it in different contexts.

The datasets used in our project can be found in the dataset_files folder.

Our main evaluation scripts are contained in the src directory with sample script wrappers in src/eval_scripts.

Other main code is split into various util files:

• eval_utils.py contains code for evaluating function vectors in a variety of contexts
• extract_utils.py contains functions for extracting function vectors and other relevant model activations.
• intervention_utils.py contains main functionality for intervening with function vectors during inference
• model_utils.py contains helpful functions for loading models & tokenizers from huggingface
• prompt_utils.py contains data loading and prompt creation functionality

The preprint can be cited as follows

@article{todd2023function,
    title={Function Vectors in Large Language Models}, 
    author={Eric Todd and Millicent L. Li and Arnab Sen Sharma and Aaron Mueller and Byron C. Wallace and David Bau},
    year={2023},
    eprint={2310.15213},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}