AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
MultiGhostBench is a multilingual benchmark for authorship attribution of long‑form text generated by large language models. It contains 928 books produced by five recent LLMs in six languages and three scripts, each averaging about 59,000 words, and is designed to test attribution methods under domain, author, and language shifts. Experiments show that no single attribution method dominates across all settings, with performance generally dropping under distribution shifts, while transformer‑based detectors retain generator information across languages but vary in transfer effectiveness, and statistical/fingerprint detectors are more language‑dependent.
MultiGhostBench is a multilingual benchmark for authorship attribution of long-form text generated by large language models. It contains 928 books produced by five recent LLMs in six languages and three scripts, each averaging about 59,000 words, and is designed to test attribution methods under domain, author, and language shifts. Experiments show that no single attribution method dominates across all settings, with performance generally dropping under distribution shifts, and that transformer-based detectors retain generator information across languages while statistical and fingerprint-based detectors are more language‑dependent.
arXiv:2603.15034v2 Announce Type: replace-cross Abstract: This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts. E...
arXiv:2608. 19746v1 Announce Type: new Abstract: Personalized text generation aims to make LLMs write in a specific individual's style, yet existing benchmarks measure task accuracy or preference alignment rather than whether the model's output actually resembles the target author's writing.
Authorship verification (AV) assumes that an author's writing style remains sufficiently stable to distinguish it from that of other writers. In practice, however, this assumption is challenged by dis...
arXiv:2508. 01656v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.