Authorship Verification of Transcribed German-Language Videos
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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.
The paper introduces methods to improve speech recognition for multilingual video transcription using Whisper-based tools, targeting cross‑cultural understanding. It reports an average transcription error rate of 30% across seven languages, which can be lowered to 20% with modest fine‑tuning. The authors also release associated speech and metadata to aid community refinement of these techniques.
arXiv:2609.06771v1 Announce Type: cross Abstract: Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation i...
The study investigates how different transcript compression methods affect large language model (LLM) detection of medical misinformation in Japanese YouTube videos. Four input designs were compared: full transcripts, LLM-generated summaries, RAPTOR-based retrieval‑augmented generation (RAG), and a Screening approach that extracts candidate medical sentences. Results show that full transcripts yield the best classification accuracy, while all compressed inputs increase false negatives, with summaries causing the largest performance drop and Screening performing best among compressed methods yet still missing many relevant sentences. Linguistic analysis indicates that compression reduces affective, social, temporal, cognitive, and conversational cues, and increases the prominence of institutional and technical terms, thereby making fake videos appear more coherent and authoritative.
TRILOGUE is a new trilingual benchmark for spoken dialogue fact‑checking, covering English, Russian, and Kazakh. It includes almost 12,000 dialogues, 187,000 turns, and 390 hours of paired audio with ASR transcripts and word‑level timestamps, as well as nearly 5,000 human‑recorded Russian and Kazakh files. The dataset supports tasks such as claim check‑worthiness detection, evidence retrieval, and claim verification under various input conditions, and baseline experiments reveal challenges with ASR errors and cross‑lingual transfer, especially for Kazakh.