Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study

17 Aug 2020 · Dara Bahri, Yi Tay, Che Zheng, Donald Metzler, Cliff Brunk, Andrew Tomkins ·

Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Our work is twofold: firstly we demonstrate via human evaluation that classifiers trained to discriminate between human and machine-generated text emerge as unsupervised predictors of "page quality", able to detect low quality content without any training. This enables fast bootstrapping of quality indicators in a low-resource setting. Secondly, curious to understand the prevalence and nature of low quality pages in the wild, we conduct extensive qualitative and quantitative analysis over 500 million web articles, making this the largest-scale study ever conducted on the topic.

PDF Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

Adam • Attention Dropout • BPE • Cosine Annealing • Dense Connections • Discriminative Fine-Tuning • Dropout • GELU • GPT-2 • Layer Normalization • Linear Layer • Linear Warmup With Cosine Annealing • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • Softmax • Weight Decay

Edit Social Preview

Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods

Add Remove