README.md

metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - en
  - de
  - es
  - fr
  - ru
  - zh
  - ja
  - ar
  - hi
  - bn
tags:
  - ai-text-detection
  - llm-attribution
  - cloze-congruence
  - multilingual
  - model-provenance
  - synthetic-text
size_categories:
  - 1K<n<10K

ClozeCongruence 3.0: Multilingual AI Text Forensics & Provenance Benchmark

Official reproducible benchmark datasets accompanying the research paper: "ClozeCongruence 3.0: Cross-Lingual Macro-Discourse Reconstruction and Dynamic Burstiness Gating for Zero-Shot AI Text Forensics across 10 Typologically Diverse Languages" (Debdip Bandyopadhyay, 2026).

Benchmark Splits

This benchmark spans 10 global languages across 6 typological writing scripts:

• Latin: English (en), German (de), Spanish (es), French (fr)
• Cyrillic: Russian (ru)
• CJK: Simplified Chinese (zh), Japanese (ja)
• Arabic: Modern Standard Arabic (ar)
• Devanagari / Indic: Hindi (hi), Bengali (bn)

Included Datasets

1. data/multilingual_longform_benchmark.json:

   • 70 scholarly documents (42-45 paragraphs each; 3,120 paragraphs, 138,008 words).
   • Evaluated across 7 author classes: Human Academic, OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, Google Gemini 3.7 Flash, DeepSeek-R1, Alibaba Qwen 2.5 72B, and Meta Llama 3.3 70B.

2. data/real_world_downloaded_human_benchmark.json:

   • 10 authentic academic treatises downloaded from Wikipedia across all 10 target languages (447 paragraphs, 19,161 words) establishing real-world 0.00% False Positive Rate (FPR).

3. data/multilingual_mixed_human_ai_benchmark.json:

   • 30 long-form documents across 3 realistic human-AI collaboration regimes (50/50 interleaved, 25/75, and 75/25).

Key Empirical Metrics

• Mean 10-Language AUROC: 98.92%
• Native Human False Positive Rate (FPR): 0.00% (Zero false accusations)
• Top-1 LLM Attribution Accuracy: 99.71%
• Cryptographic Provenance: ISO/IEC 27037 Tamper-Evident SHA-256 and Ed25519 digital certificates.

Citation

@article{bandyopadhyay2026cloze3,
  title={ClozeCongruence 3.0: Cross-Lingual Macro-Discourse Reconstruction and Dynamic Burstiness Gating for Zero-Shot AI Text Forensics across 10 Typologically Diverse Languages},
  author={Bandyopadhyay, Debdip},
  journal={Transactions on Machine Learning Research},
  year={2026}
}