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New benchmark tackles long-form LLM text attribution across languages

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced MultiGhostBench, a new multilingual benchmark designed to evaluate the attribution of long-form text generated by Large Language Models (LLMs). This benchmark includes 928 books across six languages and three scripts, with an average length of approximately 59,000 words, and supports evaluation under various distribution shifts like domain, author, and language changes. Initial evaluations indicate that current attribution methods struggle to perform consistently across different settings, with performance degrading under distribution shifts, though Transformer-based detectors show some cross-lingual capabilities. AI

IMPACT This benchmark could drive advancements in detecting AI-generated content, crucial for academic integrity and combating misinformation.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for LLM text attribution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark tackles long-form LLM text attribution across languages

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The cluster describes a new academic paper introducing a benchmark for LLM text attribution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Matteo Greco, Anudeex Shetty, Andrea Tagarelli, Jey Han Lau ·

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

    arXiv:2609.02379v1 Announce Type: cross Abstract: While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled settings, or relatively outdated models, with the few multilingual studies consid…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jey Han Lau ·

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

    While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled settings, or relatively outdated models, with the few multilingual studies considering only relatively short texts. We introduce Mu…

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