arXiv (All)
open access
Large Language Model Turnover Undermines Screening for Artificial Intelligence-Assisted Scientific Writing
Journals and conferences have begun to screen submitted manuscripts for text written using large language models (LLMs). The reliability of this screening rests on benchmark evaluations against a fixed set of LLM versions, while the versions in actual use keep changing. Here we quantify how this LLM
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
- Model Retirement Creates Reproducibility Risk in Biomedical AI Publications#927966
- The Ability of Generative Artificial Intelligence to Evaluate the Quality of Scholarly Output#1360410
- SumRank: Aligning Summarization Models for Long-Document Listwise Reranking#998158
- How Do LLM-Generated Texts Impact Term-Based Retrieval Models?#1355922
- A Fragility Spectrum for Recursive Language-Model Training#998701
Record · ID 1248879
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