# Papilusion at DAGPap24: Paper or Illusion? Detecting AI-generated Scientific Papers

arXiv: https://arxiv.org/abs/2407.17629 · HTML: https://mlsanity.com/paper/2407.17629/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals](https://mlsanity.com/paper/2107.06751/) (2021)

## Built on by (most cited first, 1 of 1)

- [BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?](https://mlsanity.com/paper/2510.18003/) (2025)

## Abstract (arXiv)

This paper presents Papilusion, an AI-generated scientific text detector developed within the DAGPap24 shared task on detecting automatically generated scientific papers. We propose an ensemble-based approach and conduct ablation studies to analyze the effect of the detector configurations on the performance. Papilusion is ranked 6th on the leaderboard, and we improve our performance after the competition ended, achieving 99.46 (+9.63) of the F1-score on the official test set.