drewskidang / synthetic_contract_retrieval

Code for Gen-IR@SIGIR23 Paper "GPT-4 Synthetic Data Improves Generalizability For Contract Clause Retrieval"

synthetic_contract_retrieval

Code for Gen-IR@SIGIR23 Paper "GPT-4 Synthetic Data Improves Generalizability For Contract Clause Retrieval"

To generate data:

• Create a file named data_generation/credentials.json with your OpenAI key and organization
• Run data_generation/synthetic_datagen.ipynb to generate synthetic datasets from GPT-4
• Run data_generation/preprocess_cuad_ds.ipynb to create the baseline CUAD train and test sets both the cross and dual encoders.
• Run data_generation/preprocess_synthetic_datasets.ipynb to create the synthetic datasets (synthetic, synthetic discriminative and SQUAD).

Training and evaluating a model

To train and evaluate an individual model, run:

• sh ./train_eval_scripts/cross_encoder/train.sh for a cross encoder
• sh ./train_eval_scripts/dual_encoder/train.sh for a dual encoder

Modify the train_folder and train_file as appropriate in the data directory. The scripts are default to run five times, this can be modified in the for loop. Once training and evaluation is done, the model will be saved in the directory of the train script while the metrics will be saved as .jsonl file in a ./results folder in the top level directory of the repository. The results should look something like this (example):

{"0.9": {"micro_average_precision": 0.12920440829722774, "micro_average_portion_dataset": 0.41747621452929423, "micro_average_recall": 0.9001152516327314}}
{"0.95": {"micro_average_precision": 0.10629680006204627, "micro_average_portion_dataset": 0.5228543869134854, "micro_average_recall": 0.9500576258163658}}
{"0.97": {"micro_average_precision": 0.09646806820065935, "micro_average_portion_dataset": 0.5821436151544038, "micro_average_recall": 0.9700345754898194}}