The study examines how generative AI has reshaped the public web in under five years, using over 5 million crawlable pages. It finds that AI-generated text has risen sharply but unevenly, with higher prevalence on English-language and .com sites, lower on .gov and .edu, and varying by a page’s centrality in the web graph. The authors employ multiple detection tools and a Bayesian hierarchical model to estimate prevalence while accounting for detector errors.
By Adam Visokay, Soumyakanti Pan, Molly Griston, Polina Iasakova, Mark Nepf, Denis Peskoff, Riya Sinha, Jikai Sun, Isabelle Thiara, Letao Wang, Yiwei Wu, Lauren Yan, Tyler H. McCormick
In less than five years, generative artificial intelligence (AI) has dramatically reshaped the public-facing web. Using a sample of more than 5 million crawlable webpages, we demonstrate that the shar...
arXiv:2608. 16824v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines.
By Junjie Chu, Ye Leng, Mingjie Li, Yun Shen, Xinyue Shen, Yang Zhang
arXiv:2607. 15267v1 Announce Type: new Abstract: Poisoning pretraining data can introduce harmful behaviors to LMs that are difficult to detect and mitigate.
By Victoria Graf, Hannaneh Hajishirzi, Noah A. Smith, David Kohlbrenner, Kyle Lo
The paper introduces a method to detect which web scrapers feed data to large language models (LLMs) by deploying dynamic websites that issue unique canary tokens to each scraper. By querying LLMs for information about these sites, the authors can identify when an LLM consistently outputs the unique tokens, indicating exposure to a specific scraper. Experiments on 22 production LLM systems show the technique reliably uncovers both known and undisclosed scrapers, offering a tool for third parties to monitor and control unwanted web scraping.
By Steven Seiden, Triss Ren, Caroline Zhang, Taein Kim, Enze Liu, Emily Wenger
arXiv:2609.15369v1 Announce Type: new
Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
By Jochen Madler (Sitefire)
The study examines how to allocate web‑crawling resources to gather higher‑quality data for large language model pretraining. By combining document‑level quality scores, web‑graph centrality, and existing crawl coverage, it finds that most variation in quality is between hosts and that sampling a few pages from a host can predict the quality of the rest. The results suggest that depth‑based sampling of high‑scoring hosts yields far more high‑quality pages per fetch than random or centrality‑based strategies, while overall host coverage shows little correlation with quality.
By Malte Ostendorff
arXiv:2610.34572v1 Announce Type: new
Abstract: The largest open German text datasets are built from broad web crawls, which provide scale but limited coverage of specific domains. We introduce Refin...
By Maximilian Idahl, Oleh Astappiev, Benedikt Droste, Robert Scholz, Bj\"orn Pl\"uster, Jan Philipp Harries, Robert J\"aschke, Wolfgang Nejdl
The paper demonstrates that large language models (LLMs) used for forecasting real‑world events can be manipulated by simply publishing new articles, even without direct access to the model or its retriever. By injecting a small number of targeted news pieces into a common crawl corpus, an adversary can flip over half of the forecast probabilities and significantly degrade forecast accuracy. The study also shows that common defense strategies can be cheaply bypassed, highlighting the vulnerability of probabilistic LLM judgments to information‑supply‑chain attacks.
By Yuan Lu, Yukuan Zhang
arXiv:2608. 08994v1 Announce Type: cross Abstract: Retrieving relevant evidence from noisy web data is challenging, particularly in sensitive domains containing incomplete reports, heterogeneous language, and irrelevant content.
By Joshua Castillo, Santosh Nukavarapu, Ravi Mukkamala
arXiv:2609.01182v1 Announce Type: new
Abstract: Flagship language models appear saturated on benchmarks like MMLU (Hendrycks et al., 2021), scoring above 90% - yet benchmarks test only what the exper...
By Muhammed Saeed, Simon Razniewski
arXiv:2609.05766v1 Announce Type: cross
Abstract: The dominant approach to building domain-specific pretraining corpora is to filter large web archives such as CommonCrawl. This works well for popula...
By Chirag Garg, Eelaaf Zahid, Farhan Ahmed, Jay Pankaj Gala, Eric Butler, Heiko Ludwig