repe_eval

Language Model Representation Evaluation (RepE Eval)

Overview

This framework provides an approach to evaluate the representations of LLMs on different standard benchmarks. For more details about evaluation, please check out our RepE paper.

Install

To install repe, run:

git clone https://github.com/andyzoujm/representation-engineering.git
cd representation-engineering
pip install -e .

Basic Usage

To evaluate a language model's representations on a specific task, use the following command:

python rep_reading_eval.py \
    --model_name_or_path $model_name_or_path \
    --task $task \
    --ntrain $ntrain \
    --seed $seed

Examples

For hands-on examples on how to evaluate both decoder and encoder models, please refer to our example notebooks. Additionally, command line scripts are provided to reproduce the results reported in our RepE paper.

Citation

If you find this useful in your research, please consider citing:

@misc{zou2023transparency,
      title={Representation Engineering: A Top-Down Approach to AI Transparency}, 
      author={Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, Zico Kolter, Dan Hendrycks},
      year={2023},
      eprint={2310.01405},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}