---
title: "Defending Against Neural Fake News — Zellers 2019"
description: "Large text generators could let adversaries produce large amounts of fabricated news written to look like it comes from real outlets."
authors: ["Rowan Zellers", "Ari Holtzman", "Hannah Rashkin", "Yonatan Bisk", "Ali Farhadi", "Franziska Roesner", "Yejin Choi"]
year: 2019
venue: "arXiv:1905.12616"
source: "https://arxiv.org/abs/1905.12616"
doi: "10.48550/arxiv.1905.12616"
arxiv: "1905.12616"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/"
---
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# Defending Against Neural Fake News

**Authors:** [Rowan Zellers](https://md.research.elamur.ai/free/en/authors/rowan-zellers/), [Ari Holtzman](https://md.research.elamur.ai/free/en/authors/ari-holtzman/), [Hannah Rashkin](https://md.research.elamur.ai/free/en/authors/hannah-rashkin/), [Yonatan Bisk](https://md.research.elamur.ai/free/en/authors/yonatan-bisk/), [Ali Farhadi](https://md.research.elamur.ai/free/en/authors/ali-farhadi/), Franziska Roesner, [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) · **Year:** 2019 · **Venue:** arXiv:1905.12616 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1905.12616) · [PDF](https://arxiv.org/pdf/1905.12616) · [DOI 10.48550/arxiv.1905.12616](https://doi.org/10.48550/arxiv.1905.12616) · [arXiv 1905.12616](https://arxiv.org/abs/1905.12616) · [OpenAlex](https://openalex.org/W2947813521)

**Citations:** 79 citations (openalex-snapshot · 2026-10-14) · [381](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Large text generators could let adversaries produce large amounts of fabricated news written to look like it comes from real outlets. The authors take the threat-modeling approach used in computer security. They set up disinformation as a contest between two players. One is an adversary who generates fake articles. The other is a verifier that sees plenty of genuine news but only a small sample of one adversary's fakes. To study this threat, they build Grover, a Transformer language model that writes a complete article: domain, date, authors, headline and body. Each field has its own start and end tokens, so any subset of fields can be given as context and any missing field can be generated. Human raters judged Grover's rewritten propaganda to be more trustworthy than disinformation written by people. For detection, Grover used as a classifier reaches 92% accuracy. The best other detectors, deep pretrained models such as BERT and GPT-2, reach 73%. The authors trace this gap to artifacts left by exposure bias and by truncated sampling. They argue that publicly releasing strong generators is essential for defense.

## Key points
- Detection setup: the verifier is trained on 5,000 Grover generations plus unlimited real news. Grover as a discriminator scores 92%, compared with 73% for the strongest non-Grover baseline.
- The model comes in three sizes (Grover-Base, Grover-Large, Grover-Mega) and is trained on RealNews, a news corpus collected from Common Crawl. Collecting the data cost about $10k in AWS credits. Training Grover-Mega took about two weeks on TPU v3 cores and cost about $25k.
- With nucleus (top-p) sampling, fakes are hardest to detect at a threshold of roughly p=0.94–0.98. BERT's worst-case threshold is lower than Grover's. The authors read this as a sign that left-to-right models are better at spotting where the probability tail was truncated.
- Fields are generated in a fixed order (domain → date → authors → headline → body). Grover can also regenerate an earlier field, for example rewriting a headline after the body exists.
- The authors released the models to researchers. They recommend that platforms screen articles with an ensemble of generator-based detectors and keep humans in the loop.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

381 papers of our library cite it (of the 140673 whose bibliographies we read); 37 of them have a page here, 344 not yet.

- [Automatic Story Generation: Challenges and Attempts](https://md.research.elamur.ai/free/en/papers/alabdulkarim-2021-automatic-story-generation-challenges-attempts/)
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- [On the Opportunities and Risks of Foundation Models](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
- [Extracting Training Data from Large Language Models](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
- [Enabling Language Models to Fill in the Blanks](https://md.research.elamur.ai/free/en/papers/donahue-2020-enabling-language-models-fill-blanks/)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
- [A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/)
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## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{zellers2019defending,
  title = {{Defending Against Neural Fake News}},
  author = {Zellers, Rowan and Holtzman, Ari and Rashkin, Hannah and Bisk, Yonatan and Farhadi, Ali and Roesner, Franziska and Choi, Yejin},
  year = {2019},
  doi = {10.48550/arxiv.1905.12616},
  eprint = {1905.12616},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1905.12616}
}
```

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