Awesome

SunGen

This repository contains the code for our paper “SunGen: Self-Guided High-Quality Data Generation in Efficient Zero-Shot Learning”.

Data generation

For data generation via PLM, the implementation is built on the source code from ZeroGen. For movie review sentiment classification tasks (imdb, sst-2, rotten tomato), we use the same prompts as ZeroGen. For other tasks, we provide the detailed prompts for each task in this repository under ./tasks/.

We provide sample codes for yelp data generation:

(1) generate restaurant name

python main.py --reload_model  --task_file tasks/yelp/yelp-x1.json --input_file_type plain --output_dir yelp/output/yelp-x1-gen/ --model_name  gpt2-xl --small_model_name distilbert -base-uncased  --min_length 1 --max_length 5  --top_k 0 --top_p 0.9 --decay_constant 200 --batch_size 2048  --train_batch_size 32 --learning_rate 2e-5 --num_entries_per_input 500000

(2) generate restaurant review dataes given restaurant name

python main.py --reload_model  --task_file tasks/yelp/yelp-x2.json --output_dir   yelp/output/yelp-x1/ --input_file_type 'plain' --input_file tasks/subj/res_names.txt --model_name  gpt2-xl --small_model_name distilbert-base-uncased  --min_length 10 --max_length 100  --top_k 0 --top_p 0.9 --decay_constant 200 --batch_size 180  --train_batch_size 32 --learning_rate 2e-5 --num_entries_per_input 1000000

More details can be found on our paper.

Run with generated data

After dataset generation, we save the synthetic dataset at train.jsonl. The file is in json line format (e.g., {"idx": 0, "text": "The Book of Mormon Musical brings all the drama and excitement of a real revival of the Broadway production to the big screen.", "label": 0}). We provide some sample synthetic set and standard sets in this google drive link.

To learn the sample reweighs using LSTM as TAM, please use the following script.

python run_reweight.py --gold_data_path data/imdb/std/ --syn_data_path data/imdb/gpt2-xl/ --task_name imdb --num_use_samples_inner 1000000 --num_use_samples_outer 50000 --epoch_converge 1 --outer_lr 2.5e-1 --inner_lr 1e-3 --seed 12345 --backward_batch_size 4096 --wandb --outer_obj combined --inner_obj ce --init_label 10 --theta_upper_lim 1 --check_ft_every 5 --epoch_converge_fully_train 5 --threshold 0.9 --optim Adam --max_outer_iter 100 --hard --init_theta 1 --subset_outer --use_sigmoid --disable_outer_scheduler --shuffle_train

Acknowledgement

If you find our code useful, please cite our paper:

@inproceedings{
gao2023selfguided,
title={Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning},
author={Jiahui Gao and Renjie Pi and LIN Yong and Hang Xu and Jiacheng Ye and Zhiyong Wu and WEIZHONG ZHANG and Xiaodan Liang and Zhenguo Li and Lingpeng Kong},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=h5OpjGd_lo6}
}

@inproceedings{ye-etal-2022-progen,
title = "ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback",
author = "Ye, Jiacheng and Gao, Jiahui and Wu, Zhiyong and Feng, Jiangtao and Yu,Tao     		and Kong, Lingpeng",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.269",
pages = "3671--3683"
}

@inproceedings{ye-etal-2022-zerogen,
title = "{Z}ero{G}en: Efficient Zero-shot Learning via Dataset Generation",
author = "Ye, Jiacheng  and Gao, Jiahui  and Li, Qintong  and Xu, Hang  and Feng, Jiangtao  and Wu, Zhiyong  and Yu, Tao  and Kong, Lingpeng",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
year = "2022"
}