AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths
MaoXun Huang, Zhenxing Zhang, Claire Cardie
cs.CL · cs.AI
Quick verdict
Claims multilingual authorship benchmark (428K docs, 47 zero-shot models); valuable, but no significance tests, non-author-disjoint splits, heavy source skew.
Summary
The paper introduces AuthBench, a benchmark for authorship representation combining 428,150 documents by 153,825 authors across ten languages, nine primary (66 fine-grained) genres, and four token-length buckets, with standardized splits for two tasks: same-author retrieval (attribution) and pairwise verification. Seventeen public sources are integrated through a five-stage pipeline (normalization, quality filtering, SimHash-based redundancy reduction, language audit, balanced sampling), and 47 neural models plus three lexical/compression baselines are evaluated zero-shot under one protocol. Headline results: multilingual-e5-large leads retrieval with 0.258 Success@5, while llama3.1-8b-instruct leads verification with 0.076 EER and 0.968 ROC-AUC, revealing that different model families excel at the two tasks. Difficulty varies widely: Chinese slices reach 0.425 S@5 versus 0.190/0.196 for Russian and Korean; research_paper slices hit 0.838 versus 0.088 for media_reviews; llama3.1-8b climbs from 0.135 on short to 0.543 on extra-long documents. The authors acknowledge that language, genre, and length axes are not orthogonal; four sources supply 74.4% of documents; splits are not author-disjoint, leaving residual leakage and memorization risk. No statistical significance tests or confidence intervals accompany the rankings.
Review complete
Assessment at a glance
6
risk flags identified
● high 1
● medium 3
● low 2
4 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- AuthBench is the first benchmark to jointly provide multilingual (10 languages), multi-genre (9 primary/66 fine-grained), and length-bucketed (4 buckets) coverage with standardized splits for both retrieval-based attribution and pairwise verification.
- Under a unified zero-shot protocol, authorship representation remains far from solved: the best retrieval model reaches 0.258 Success@5 and the best verifier 0.076 EER / 0.968 ROC-AUC.
- Retrieval and verification favor different model families (multilingual-e5-large vs llama3.1-8b-instruct), so the two tasks are not interchangeable probes of representation quality.
- Performance varies sharply across languages, genres, and lengths, with short documents the main bottleneck (llama3.1-8b: 0.135 to 0.543 S@5 from short to extra-long).
Strengths
Genuinely joint coverage at scale
428,150 documents / 153,825 authors across 10 languages, 9 primary (66 fine-grained) genres, and 4 length buckets, with seeded stratified splits for both retrieval and verification — no prior resource (Table 1) combines these axes; language counts in Table 2 sum exactly to 428,150.
Unified zero-shot protocol over 50 systems
One pipeline (tokenize, mean-pool last hidden states, L2-normalize, cosine) across 47 neural systems plus tfidf/ngram/ppm, with correct formal metric definitions (Appendix A.8) and a full 47-row leaderboard (Table 8).
Actionable task-split finding with systematic slices
Transparent, multi-level cleaning pipeline
Weaknesses
Zero statistical inference
Non-author-disjoint splits
Heavy source skew confounds slices
No authorship-specialized baselines
Table 1 count anomalies
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Scope & Claims
AuthBench (arXiv 2609.06771) is a benchmark paper for authorship representation. The claimed contribution is the first resource jointly covering multilinguality (10 languages), genre breadth (9 primary, 66 fine-grained), and length control (4 token-length buckets) with standardized splits for two tasks: same-author retrieval (attribution) and pairwise verification. The corpus holds 428,150 documents by 153,825 authors (avg 2.78 docs/author, Table 2), built from 17 public sources plus the authors' own crawls (Tables 15–16). Evaluation covers 47 neural models (6 instruction-tuned LLMs, 5 base LLMs, 2 instruction-tuned embedding models, 34 embedding models; Table 7) plus character-TF-IDF, stylometric n-gram, and PPM-style compression baselines, all zero-shot. Abstract claims are internally consistent with the results tables.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 4 strengths & 5 weaknesses, analyzed in detail
- 6 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is AuthBench?
A large-scale public benchmark for authorship representation: 428,150 documents by 153,825 authors across ten languages, nine primary genres (66 fine-grained), and four length buckets, with standardized splits for same-author retrieval and authorship verification, released on GitHub and Hugging Face.
Which models perform best on AuthBench?
In zero-shot evaluation, multilingual-e5-large is the best retrieval model (0.258 Success@5), while llama3.1-8b-instruct is the best verification model (0.076 EER, 0.968 ROC-AUC) — different model families lead the two tasks.
Is the AuthBench dataset freely downloadable?
Partially. The toolkit, splits and benchmark data are released via GitHub and Hugging Face, but licensing-sensitive sources (e.g., Amazon Reviews Multi, Douban, YouTube comments) are provided in a manifest-only mode that requires users to reconstruct text from original releases.
Why does the paper say authorship representation is 'far from solved'?
Even the best zero-shot model finds a same-author document in the top 5 for only 25.8% of queries, short documents are especially hard (llama3.1-8b scores 0.135 S@5 there), and performance collapses on noisy review genres (best 0.088 S@5 on media_reviews).
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Chart-embedded values (Figures 4–11, e.g., Fig 10 length boxplots, Fig 11 subgenre donuts) are only partially recoverable from PDF extraction; visual-only data could not be independently verified.
- No truncation was observed: the extraction runs from the title through Appendix E Table 17, and all numbers cited in this review were cross-checked against Tables 2–14.
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