Hugging Face Trending Papers
Sep 2

MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts

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.

arXiv AI
Sep 3

MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts

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.

By Matteo Greco, Anudeex Shetty, Andrea Tagarelli, Jey Han Lau
arXiv Computation and Language
Sep 11

Whisper-Based Speech Transcription from Videos Across Multiple Languages for Cross-Cultural Understanding

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.

By Michael Picheny
arXiv Computation and Language
Sep 28

Effects of Transcript Compression on LLM-based Medical Misinformation Detection in Japanese YouTube Videos

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.

By Yuya Wake, Sho Tsugawa, Toshiyuki Amagasa
arXiv Computation and Language
Sep 7

TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio

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.

By Chaewan Chun, Meruyert Aristombayeva, Jiyoung Choi, Mahjabin Nahar, Delvin Ce Zhang, Dongwon Lee