arXiv (All)
open access
A Training-free Method for LLM Text Attribution
Verifying the provenance of text is increasingly important for firms, educational institutions, and online platforms as Large Language Models (LLMs) produce output that is nearly indistinguishable from human-generated content. We study the problem of determining whether a given text was generated by
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Do LLM Attribution Metrics Transfer? Auditing Retrieval-Augmented Generation Evaluation Across Datasets and Constructs#1013597
- When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer#974451
- TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models#1155770
- MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts#632888
- Who Wrote the Book? Detecting and Attributing LLM Ghostwriters#1156313
Record · ID 999636
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive