# TMLab: Generative Enhanced Model (GEM) for adversarial attacks

arXiv: https://arxiv.org/abs/1910.00337 · HTML: https://mlsanity.com/paper/1910.00337/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [DialFact: A Benchmark for Fact-Checking in Dialogue](https://mlsanity.com/paper/2110.08222/) (2021)
- [Fact Checking with Insufficient Evidence](https://mlsanity.com/paper/2204.02007/) (2022)
- [Adversarial Attacks Against Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2509.08463/) (2025)
- [LLM-Based Adversarial Persuasion Attacks on Fact-Checking Systems](https://mlsanity.com/paper/2601.16890/) (2026)
- [AtomEval: Validity-Aware Atomic Evaluation of Adversarial Claim Rewriting in Fact Verification](https://mlsanity.com/paper/2604.07967/) (2026)

## Abstract (arXiv)

We present our Generative Enhanced Model (GEM) that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended language model developed upon GPT-2 architecture. The addition of novel target vocabulary input to the already existing context input enabled controlled text generation. The training procedure resulted in creating a model that inherited the knowledge of pretrained GPT-2, and therefore was ready to generate natural-like English sentences in the task domain with some additional control. As a result, GEM generated malicious claims that mixed facts from various articles, so it became difficult to classify their truthfulness.