IndicDetect: Evaluating Cross-Lingual LLM-Generated Text Detection for Hindi, Telugu, and Tamil

• URL: http://arxiv.org/abs/2608.29919v1
• Date: Sun, 30 Aug 2026 17:21:55 GMT
• Title: IndicDetect: Evaluating Cross-Lingual LLM-Generated Text Detection for Hindi, Telugu, and Tamil
• Abstract summary: We present a benchmark for AI-generated text detection in Hindi, Telugu, and Tamil.<n>IndicDetect is designed to assess the robustness of detectors under realistic distribution shifts.
• Score: 32.21285778019889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of LLMs has further heightened the need to develop dependable AI-generated text detection, especially beyond English. Nevertheless, current benchmarks pay little attention to Indic languages and test detectors in idealized settings that do not represent the real world. We present a generalized benchmark for AI-generated text detection in Hindi, Telugu, and Tamil, which we call IndicDetect, designed to assess the robustness of detectors under realistic distribution shifts. IndicDetect comprises highly curated human-written texts matched with LLM-generated counterparts across various domains and generators, and systematically evaluates detectors in the presence of domain shift, generator shift, and adversarial perturbation. Using a single and repeatable evaluation scheme, we evaluate a wide range of statistical and neural detectors. We find substantial robustness failures: supervised neural detectors perform well in-distribution, while training-free methods degrade considerably under unseen generators and adversarial attacks. The severity of these failures varies across languages, with Hindi exhibiting the largest overall degradation under adversarial perturbations. These results highlight that the primary weakness of existing detectors in Indic settings lies in their robustness, not in their peak accuracy. IndicDetect provides standard data splits, an evaluation protocol, and baselines to establish a robust, language-aware foundation for AI-generated text detection in Indic scripts.

Related papers

• Base Models Look Human To AI Detectors [68.7723287560362]
  We show that generated text from base models is often judged overwhelmingly human, whereas text generated by their instruction-tuned counterparts is not.<n>We propose Humanization by Iterative Paraphrasing (HIP), a detector-agnostic pipeline that minimally fine-tunes a base model into a paraphraser and applies it iteratively.<n>Our findings suggest that current detectors are tracking artifacts of instruction tuning and local context more than any in notion of machine-generated text.
  arXiv  Detail & Related papers  (2026-05-19T08:13:12Z)
• DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection [68.92080038934164]
  This study introduces DetectRL-X, a benchmark designed to evaluate advanced detectors across 8 dimensions.<n>The benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to misuse.
  arXiv  Detail & Related papers  (2026-05-15T01:29:26Z)
• Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection [71.59834293521074]
  We develop a framework to distinguish between human-authored and machine-generated text.<n>Our method achieves 98.3% AUROC and AUPR with only 8.9% FPR95 on DeepFake dataset.<n>Code, pretrained weights, and demo will be released.
  arXiv  Detail & Related papers  (2025-10-07T08:14:45Z)
• Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors [4.7713095161046555]
  We present a pipeline to test the resilience of state-of-the-art MGT detectors to linguistically informed adversarial attacks.<n>We fine-tune language models to shift the MGT style toward human-written text (HWT)<n>This exploits the detectors' reliance on stylistic clues, making new generations more challenging to detect.
  arXiv  Detail & Related papers  (2025-05-30T12:33:30Z)
• Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors [77.82885394684202]
  We propose textbfContrastive textbfParaphrase textbfAttack (CoPA), a training-free method that effectively deceives text detectors.<n>CoPA constructs an auxiliary machine-like word distribution as a contrast to the human-like distribution generated by large language models.<n>Our theoretical analysis suggests the superiority of the proposed attack.
  arXiv  Detail & Related papers  (2025-05-21T10:08:39Z)
• A Practical Examination of AI-Generated Text Detectors for Large Language Models [25.919278893876193]
  Machine-generated content detectors claim to identify such text under various conditions and from any language model.<n>This paper critically evaluates these claims by assessing several popular detectors on a range of domains, datasets, and models that these detectors have not previously encountered.
  arXiv  Detail & Related papers  (2024-12-06T15:56:11Z)
• Navigating the Shadows: Unveiling Effective Disturbances for Modern AI Content Detectors [24.954755569786396]
  AI-text detection has emerged to distinguish between human and machine-generated content. Recent research indicates that these detection systems often lack robustness and struggle to effectively differentiate perturbed texts. Our work simulates real-world scenarios in both informal and professional writing, exploring the out-of-the-box performance of current detectors.
  arXiv  Detail & Related papers  (2024-06-13T08:37:01Z)
• Red Teaming Language Model Detectors with Language Models [114.36392560711022]
  Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. Recent works have proposed algorithms to detect LLM-generated text and protect LLMs. We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
  arXiv  Detail & Related papers  (2023-05-31T10:08:37Z)
• Can AI-Generated Text be Reliably Detected? [50.95804851595018]
  Large Language Models (LLMs) perform impressively well in various applications.<n>The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use.<n>We stress-test the robustness of these AI text detectors in the presence of an attacker.
  arXiv  Detail & Related papers  (2023-03-17T17:53:19Z)
• MOST: A Multi-Oriented Scene Text Detector with Localization Refinement [67.35280008722255]
  We propose a new algorithm for scene text detection, which puts forward a set of strategies to significantly improve the quality of text localization. Specifically, a Text Feature Alignment Module (TFAM) is proposed to dynamically adjust the receptive fields of features. A Position-Aware Non-Maximum Suppression (PA-NMS) module is devised to exclude unreliable ones.
  arXiv  Detail & Related papers  (2021-04-02T14:34:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.