Code for Testing Pre-trained Language Models' Understanding of Distributivty via Causal Mediation Analysis
This repo is built based on Python>=3.8.
The dependencies can be installed by running pip install -r requirements.txt.
We release the control and intervention sets of the dataset DistNLI, which can be found at data/DistNLI/DistNLI/control.tsv and data/DistNLI/intervention.tsv respectively. Both sets do not come with golden labels due to the complication and controversy of distributivity.
We also release the list of distributive predicates dist_pred_list.csv, the full list of predicates full_pred_list.csv, and list of grammar grammar.cfg used to generate our dataset under data/DistNLI/, as well as scripts to create these examples data_generation.py under src. Note that in the distributive predicate list, the first line should be predicates that do not differentiate singular and plural subjects, and the second line should be predicates that do so. You can customize and create new datasets by running src/data_generation.sh.
In all results, contradiction is labeled as 0, neutral is labeled as 1, and entailment is labeled as 2. They follow the format of predicted_label, true_label, [prob_of_label_0, prob_of_label_1, prob_of_label_2].
To replicate the evaluation results, you may run:
python src/evaluate_nli.py --model_name [model_name] \
--data_path [data_path] \
--dataset_type [dataset_type]
where [model_name] can be any huggingface model, [data_path] is the path to the dataset, and [dataset_type] can be either ConjNLI, HANS, or distnli. Sample bash script can be accessed at evaluate_nli.sh.
We release the code for the CMA experiment under src. To obtain total effect and natural indirect effect, you can run run_cma.sh, in which --model_name is the model you want to use and --result_dir is the directory you save the results. If the results are saved in the directory, you can simply load them using the same arguments without running the experiment again.