This repository contains the code and data for our paper:

Whose Opinions Do Language Models Reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, Tatsunori Hashimoto
Paper: http://arxiv.org/abs/2303.17548

    @Article{santurkar2023whose,
        title={Whose Opinions Do Language Models Reflect?},
        author={Shibani Santurkar and Esin Durmus and Faisal Ladhak and Cinoo Lee and Percy Liang and Tatsunori Hashimoto},
        year={2023},
        journal={arXiv preprint arXiv:2303.17548},
    }

You can start by cloning our repository and following the steps below.

1. Download and the OpinionQA dataset in ./data. Included as part of the dataset are: (i) model_input: 1498 multiple-choice questions based on Pew American Trends Panel surveys that can be used to probe LMs, (ii) human_resp: individualized human responses for these questions from Pew, and (iii) runs: pre-computed responses for OpenAI and AI21 Labs models studied in our paper.

2. Compute human and LM opinion distributions using this notebook.

3. You can explore human-LM alignment along various axes using the following notebooks: representativeness, steerability, consistency and refusals.

4. (Optional) If you would like to query models yourself, you will need to set up the crfm-helm Python package.

Then, to obtain model responses, run:

helm-run -c src/helm/benchmark/presentation/run_specs_opinions_qa_openai_default.conf --max-eval-instances 500 --suite $SUITE
helm-run -c src/helm/benchmark/presentation/run_specs_opinions_qa_ai21_default.conf --max-eval-instances 500 --suite $SUITE
helm-run -c src/helm/benchmark/presentation/run_specs_opinions_qa_openai_steer.conf --max-eval-instances 50000 --suite $SUITE
helm-run -c src/helm/benchmark/presentation/run_specs_opinions_qa_ai21_steer.conf --max-eval-instances 50000 --suite $SUITE

Shibani Santurkar