Papers › ZeroGen: Efficient Zero-shot Learning via Dataset Generation

ZeroGen: Efficient Zero-shot Learning via Dataset Generation

16 Feb 2022arXiv:2202.07922archive 2025-07-28

Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong

There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient zero-short learning method, \textsc{ZeroGen}. Given a zero-shot task, we first generate a dataset from scratch using PLMs in an unsupervised manner. Then, we train a tiny task model (e.g., LSTM) under the supervision of the synthesized dataset. This approach allows highly efficient inference as the final task model only has orders of magnitude fewer parameters comparing to PLMs (e.g., GPT2-XL). Apart from being annotation-free and efficient, we argue that \textsc{ZeroGen} can also provide useful insights from the perspective of data-free model-agnostic knowledge distillation, and unreferenced text generation evaluation. Experiments and analysis on different NLP tasks, namely, text classification, question answering, and natural language inference, show the effectiveness of \textsc{ZeroGen}.

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HKUNLP/zerogen officialmentioned in papermentioned on GitHubpytorch report
hkunlp/symgen mentioned on GitHubApache-2.0 report
sumilergao/sungen mentioned on GitHubpytorch report

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2ran · our draft was wrong
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build_instruction HKUNLP/ZeroGen/cls_generator.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · cc1eb13a395faad2 · report
convert_to_hf_dataset HKUNLP/ZeroGen/cls_generator.py official repository ran · our draft was wrong no licence file found · pointer only · d4d68097421ea8bc · report
postprocess_dataset HKUNLP/ZeroGen/cls_generator.py official repository ran fingerprinted no licence file found · pointer only · 2076c3e1f93c6ec0 · report
DataGenerator HKUNLP/ZeroGen/cls_generator.py official repository unverified no licence file found · pointer only · da68741c040ec3c1 · report
Processor HKUNLP/ZeroGen/cls_generator.py official repository unverified no licence file found · pointer only · 0cb532ded996631a · report
process_output HKUNLP/ZeroGen/cls_generator.py official repository unverified no licence file found · pointer only · 0fa030cbe27ed3c5 · report
save_jsonl HKUNLP/ZeroGen/cls_generator.py official repository unverified no licence file found · pointer only · 7b22d9ccf816fbb8 · report

Tasks

Data-free Knowledge DistillationDataset GenerationKnowledge DistillationNatural Language InferenceQuestion AnsweringText ClassificationText GenerationZero-Shot Learningtext-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Data-free Knowledge Distillation QNLI ZeroGen (T5-base) Accuracy 88.5 #2 of 4 Archive leaderboard report
Data-free Knowledge Distillation SQuAD ZeroGen (T5-base) Exact Match 69.4 #3 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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