This repository modifies the code from the paper Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias for syntactic analysis. We load a simple grammar (structural/grammar.avg) to populate a set of relative clause templates. Models are run as before, but with a new --structure argument (see run_profession_neuron.sh and attention_intervention_structural.sh).

You can run all the experiments for a given model by running the run_profession_neuron_experiments.py script. Just set the -model flag to the GPT-2 version you want to use and point -out_dir to the base directory for your results. The resulting CSV's will be saved in ${out_dir}/results/${date}_neuron_intervention.

We provide two scripts compute_neuron_split_total_effect and compute_neuron_total_effect that will report the total effects for a model in multiple different ways.

compute_neural_total_effect will additionally compute the correlational value between effect sizes and the bias value of the profession and generate a plot in ${out_dir}/neuron_profession_correlation.pdf.

If you want to compute the aggregate effect for each neuron, you can run compute_and_save_neuron_agg_effect.py, which will create a new file in results/${date}_neuron_intervention called ${model_name}_neuron_effects.csv with the results.

After you have run this for each of the models you want to investigate, you can run compute_neuron_effect_per_layer.py which will generate plots of the per-layer effects. One aggregate plot will be at ${out_dir}/neuron_layer_effect.pdf and a separate plot for each model will be saved at ${out_dir}/neuron_layer_effect_${model_name}.pdf.

Note: the analysis JSON files for winogender and winobias are already available under the winogender_data and winobias_data directories respectively, so you may disregard the following instructions if you wish. The raw Winogender and Winobias datasets (the non-json datasets in those same directories) were obtained from https://github.com/rudinger/winogender-schemas and from https://github.com/uclanlp/corefBias/tree/master/WinoBias/wino/data respectively.

If you wish to recreate the analysis files from scratch, you can run the attention intervention experiments for a specific configuration by running either the attention_intervention_winobias.py or attention_intervention_winogender.py scripts. The arguments are specified in the respective script in the intervene_attention method. See attention_intervention_winobias.sh or attention_intervention_winogender.sh for all possible configurations. The results will be written to the winobias_data/ or winogender_data/ directory.

Various reports can be generated from the JSON files by running attention_figures1.py, attention_figures2.py, or attention_figures3.py. See the respective script for a description of the reports generated. You may want to modify these scripts to only generate figures for a subset of configurations. The results are written as pdf files to subfolders in the results/ directory.

You can run experiments for attention head sparsity with attention_intervention_subset_selection.py using either Top-k or Greedy algorithm. Results are stored in {out_dir}/{algo}_{model_type}_{data}.pickle.

Additionally, intermediate results will be cached in {out_dir}/{algo}_intermediate_{model_type}_{data}.pickle and mean effect (for the entire model, each layer and each head) will be stored in {out_dir}/mean_effect_{model_type}_{data}.pickle.

Script takes in model_type (gpt-2 version), algo (greedy or topk), k (int), data (winobias or winogender) and out_dir (base directory for results).

python attention_intervention_subset_selection.py --model_type gpt2 --algo greedy --k 10 \ --data winobias --out_dir results

You can run experiments for neuron sparsity with neuron_intervention_subset_selection.py which outputs results in {out_dir}/{algo}_{model_type}{_layer}.pickle. If layer is specified, then neurons are only selected from the specified layer.

Additionally, the average odds ratio for each layer and each neuron will be stored in {out_dir}/marg_contrib.pickle. If {out_dir}/marg_contrib.pickle exists, script will use data from this file and not recompute.

Script takes in model_type (gpt-2 version), algo (greedy or topk), k (int), layer (-1 to select neurons from entire model and 0-12 for specific layer) and out_dir (base directory for results). Currently, only compatible with GPT-2.

python neuron_intervention_subset_selection.py --algo greedy --k 10 \ --layer -1 --out_dir results