# TRIDENT and SHAKTI: A Framework and Benchmark for Hindi AI-Text Detection

**Authors:** Shifali Agrahari, Sujit Kumar, Sanasam Ranbir Singh  
**Lab:** Indian Institute of Technology Guwahati · **Published:** 30 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.41232) · [PDF](https://arxiv.org/pdf/2610.41232) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.41232.html)

## Abstract

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.

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