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New AuthBench benchmark reveals limitations in AI authorship representation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced AuthBench, a comprehensive multilingual benchmark designed to evaluate authorship representation models across various genres and document lengths. The benchmark comprises over 428,000 documents from 153,000 individuals in ten languages, supporting tasks like authorship attribution and verification. Current models show significant limitations, with the best retrieval model achieving only 0.258 Success@5 and the best verification model reaching 0.076 EER, indicating that robust authorship representation remains a challenging problem. AI

IMPACT Highlights the ongoing challenges in developing AI models that can reliably identify authorship across diverse linguistic and stylistic contexts.

RANK_REASON The cluster describes a new academic benchmark and research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AuthBench benchmark reveals limitations in AI authorship representation

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The cluster describes a new academic benchmark and research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · MaoXun Huang, Zhenxing Zhang, Claire Cardie ·

    AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths

    arXiv:2609.06771v1 Announce Type: cross Abstract: Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation investigation, and machine-generated text detection. Yet current authorship benchm…

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