arXiv cs.CL· Bhaskar Ganesh Devalla, Junchao Wu, Nilesh Dokuparthi, Greeshma Yaluru, Tatiana Muniz Rodriguez, Lidia S. Chao, Derek F. Wong·· 12 小时前AI 评分24
IndicDetect：面向印地语、泰卢固语和泰米尔语的跨语言 LLM 生成文本检测基准
IndicDetect: Evaluating Cross-Lingual LLM-Generated Text Detection for Hindi, Telugu, and Tamil
AI 导读
研究者提出 IndicDetect，一个针对印地语、泰卢固语和泰米尔语的 LLM 生成文本检测基准，用于在真实分布偏移下评估检测器鲁棒性。该基准覆盖领域偏移、生成器偏移和对抗扰动，测试了多种统计与神经检测器：有监督神经检测器在分布内表现良好，免训练方法在未见生成器和对抗攻击下显著退化，其中印地语在对抗扰动下退化最严重。结果表明现有检测器在 Indic 场景的主要短板是鲁棒性而非峰值准确率。
正文 · 原文
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.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.29919 [cs.CL] |
| (or arXiv:2608.29919v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29919 arXiv-issued DOI via DataCite |
Submission history
From: Bhaskar Ganesh Devalla [view email]
[v1]
Sun, 30 Aug 2026 17:21:55 UTC (2,145 KB)
[v2]
Fri, 16 Oct 2026 16:26:48 UTC (1,779 KB)
来源：arXiv cs.CL · arxiv.org