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Stop DDoS Attacking the Research Community with AI-Generated Survey Papers

Jianghao Lin, Rong Shan, Jiachen Zhu, Yunjia Xi, Yong Yu, Weinan Zhang

TL;DR

This paper addresses the surge of AI-generated survey papers flooding the literature and proposes the concept of a 'survey paper DDoS attack' as a systemic risk to research integrity. It combines empirical arXiv analyses with qualitative quality concerns and detection metrics to show a post-2022 spike in AI-assisted surveys and patterns indicating superficiality and redundancy. The authors advocate rigorous human oversight, transparent disclosure of AI involvement, and the transition from static surveys to Dynamic Live Surveys as a sustainable alternative. The work highlights policy, governance, and infrastructural approaches to preserve trust, while embracing AI as a tool to enhance, not replace, expert critique and synthesis.

Abstract

Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i.e., survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community.

Stop DDoS Attacking the Research Community with AI-Generated Survey Papers

TL;DR

This paper addresses the surge of AI-generated survey papers flooding the literature and proposes the concept of a 'survey paper DDoS attack' as a systemic risk to research integrity. It combines empirical arXiv analyses with qualitative quality concerns and detection metrics to show a post-2022 spike in AI-assisted surveys and patterns indicating superficiality and redundancy. The authors advocate rigorous human oversight, transparent disclosure of AI involvement, and the transition from static surveys to Dynamic Live Surveys as a sustainable alternative. The work highlights policy, governance, and infrastructural approaches to preserve trust, while embracing AI as a tool to enhance, not replace, expert critique and synthesis.

Abstract

Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i.e., survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community.

Paper Structure

This paper contains 38 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. AI-Generated Survey Paper Explosion on arXiv
  3. Growth Trends.
  4. AI-driven Post-2022 Spike.
  5. Submission Patterns.
  6. Quality Concerns & Detection Methods
  7. Quality Concerns
  8. Structural Differences.
  9. Lack of Novel Taxonomy.
  10. Citation and Content Inaccuracy.
  11. Redundancy and Low Marginal Utility.
  12. Detection Metrics
  13. GPT-generated Phrases.
  14. Citation Overlap.
  15. Length and Repetition Patterns.
  16. ...and 23 more sections

Figures (2)

  • Figure 1: Left: The number of CS survey papers over the past 5 years. Middle: Averaged AI-generated scores of CS survey papers for the recent 5 years. Right: The number of abnormal authors detected per year. The data all witness a post-2022 spike, which is marked by the advent of ChatGPT and other advanced LLMs.
  • Figure 2: The structure of our position paper.