ZeroGen: Efficient Zero-shot Learning via Dataset Generation

By Jiacheng Ye, Jiahui Gao, Qintong Li, Hang Xu, Jiangtao Feng, Zhiyong Wu, Tao Yu, Lingpeng Kong. 2022

305 citations recorded by the source corpus.

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, 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 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 ZeroGen.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Generating Training Data with Language Models: Towards Zero-Shot Language Understanding (2022, background): “The similar setting is also explored in a concurrent study [77].”
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024, methodology): “…advancements in LLMs, which demonstrate the capability to generate fluent text on par with human output (Hartvigsen et al., 2022; Sahu et al., 2022; Ye et al., 2022a; Tang et al., 2023; Gao et al., 2023a), synthetic data produced by LLMs emerges as a viable alternative or supplement to…”
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning (2022, methodology): “, 2020) with the standard language modeling objective on the training set or using label-specific prompts (Gao et al., 2023; Meng et al., 2022a; Schick & Schütze, 2021c; Wang et al., 2021; Ye et al., 2022) to steer text generation towards the desired label.”
Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2023, background): “…with Language Model Another line of related works propose to directly distill models with LM-generated data, improving model reasoning (Zelikman et al., 2022; Hsieh et al., 2023), robustness (Chen et al., 2022a), controllability (Sclar et al., 2022), and language understanding (Ye et al., 2022).”
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2021, unknown): “, 2020c), Generation from Few-shot Models (GF) (Ye et al., 2022; Wang et al., 2021); and (iii) Hidden-space augmentation: Adversarial”
A Survey on Knowledge Distillation of Large Language Models (2024, methodology): “In the context of NLU tasks, certain studies (Ye et al., 2022; Gao et al., 2023a; Wang et al., 2021a) explore the use of labels as meta-information to synthesize corresponding samples for data augmentation.”
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (2023, methodology): “Initial explorations in this domain include SuperGen [30] and ZeroGen [55], which use LLMs for text classification and noise robust learning techniques [35, 52] to handle data As New Zealand's state governments continue to implement the Affordable Care Act, focus has turned towards the success of…”