arXiv:2607. 10020v2 Announce Type: replace-cross Abstract: We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus.
arXiv:2607. 10020v1 Announce Type: cross Abstract: We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus.
arXiv:2502.10673v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. How...
Institutional Books – Enriched Text is a 2025 release that transforms Harvard Library’s 983,004-volume collection (IB‑HL) into a multilingual, annotated dataset. The pipeline normalizes OCR text while preserving metadata, separating endmatter, detecting paragraph language, clustering duplicates, and scoring bits‑per‑byte, all wrapped in HTML‑like annotations. The resulting IB‑HL‑ET contains 217 B tokens across 983,003 volumes and 1.39 B annotated subtopic paragraphs, enabling users to customize output rather than accept a single editorial decision.
Large-scale pretraining corpora contain substantial duplicate content. Although document-level deduplication is widely used, removing subdocument-level redundancy remains challenging.
arXiv:2602. 10908v2 Announce Type: replace-cross Abstract: We present SoftMatcha 2, an ultra-fast and flexible search algorithm that enables search over trillion-scale natural language corpora in under 0.
arXiv:2605. 28510v2 Announce Type: replace-cross Abstract: Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authorship attribution, raising legal and ethical concerns around plagiarism and license compliance.
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.
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.
arXiv:2608. 03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review.
arXiv:2609.37226v1 Announce Type: cross Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as...
arXiv:2606. 04906v1 Announce Type: cross Abstract: Although it is generally agreed that AI-generated text poses a broad societal risk, there is no common understanding in the AI-generated text detection literature on what constitutes harmful use.
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.