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TRIDENT and SHAKTI: A Framework and Benchmark for Hindi AI-Text Detection — arXiv:2610.41232

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41232
License (as archived): CC0-1.0
Archived: 2026-11-03T05:22:35.915Z
SHA-256 of the archived text: 3566de219faa488331d72947455542b0c0bd9d47d72c4a24a36839c8b28cdc6d
Stable URL of this page: https://metraipolis.com/documento/dc_02aee950bc57

How to cite

TRIDENT and SHAKTI: A Framework and Benchmark for Hindi AI-Text Detection — arXiv:2610.41232 — arXiv (en), https://arxiv.org/abs/2610.41232 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_02aee950bc57 (Metraipolis archive dc_02aee950bc57, sha256 3566de219faa488331d72947455542b0c0bd9d47d72c4a24a36839c8b28cdc6d)

BibTeX

@misc{dc02aee950bc57,
  title = {TRIDENT and SHAKTI: A Framework and Benchmark for Hindi AI-Text Detection — arXiv:2610.41232},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41232},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_02aee950bc57; sha256 3566de219faa488331d72947455542b0c0bd9d47d72c4a24a36839c8b28cdc6d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41232. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As Large Language Models (LLMs) become increasingly multilingual, distinguishing human-written from AI-generated text is essential for maintaining information integrity. However, research remains English-centric, with limited benchmarks for low-resource languages such as Hindi. To address this gap, we introduce SHAKTI, a large-scale Hindi benchmark covering four genres, eight LLM families, multiple prompting strategies, and adversarial transformations. We evaluate its quality through human annotation and analyze linguistic, grammatical, and sentiment-based differences between human- and AI-generated texts. Building on these findings, we propose TRIDENT, a round-trip translation-based detection framework with three variants: TRIDENT-S (static threshold), TRIDENT-D (dynamic threshold), and TRIDENT-C (logistic regression using multi-distance RTT features). TRIDENT leverages semantic consistency between original and round-trip translated texts to identify AI-generated content. Extensive experiments show that TRIDENT outperforms conventional and supervised baselines, with TRIDENT-S achieving the best overall performance. Further evaluations across seven languages, adversarial settings, and unseen LLMs demonstrate its multilingual applicability and robustness, while highlighting sensitivity to the translation model.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.41232 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T05:18:52.104Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).