VietBinoculars: A Zero-Shot Approach for Detecting Vietnamese LLM-Generated Text

• URL: http://arxiv.org/abs/2509.26189v1
• Date: Tue, 30 Sep 2025 12:43:37 GMT
• Title: VietBinoculars: A Zero-Shot Approach for Detecting Vietnamese LLM-Generated Text
• Abstract summary: This study proposes VietBinoculars, an adaptation of the Binoculars method with optimized global thresholds.<n>We have constructed new Vietnamese AI-generated datasets to determine the optimal thresholds for VietBinoculars.<n>Results show that VietBinoculars achieves over 99% in all two domains of accuracy, F1-score, and AUC.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid development research of Large Language Models (LLMs) based on transformer architectures raises key challenges, one of them being the task of distinguishing between human-written text and LLM-generated text. As LLM-generated textual content, becomes increasingly complex over time, and resembles human writing, traditional detection methods are proving less effective, especially as the number and diversity of LLMs continue to grow with new models and versions being released at a rapid pace. This study proposes VietBinoculars, an adaptation of the Binoculars method with optimized global thresholds, to enhance the detection of Vietnamese LLM-generated text. We have constructed new Vietnamese AI-generated datasets to determine the optimal thresholds for VietBinoculars and to enable benchmarking. The results from our experiments show results show that VietBinoculars achieves over 99\% in all two domains of accuracy, F1-score, and AUC on multiple out-of-domain datasets. It outperforms the original Binoculars model, traditional detection methods, and other state-of-the-art approaches, including commercial tools such as ZeroGPT and DetectGPT, especially under specially modified prompting strategies.

Related papers

• EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles [16.37055946884028]
  EnsemJudge is a robust framework for detecting Chinese LLM-generated text.<n>We trained and evaluated our system on a carefully constructed Chinese dataset provided by NLPCC2025 Shared Task 1.
  arXiv  Detail & Related papers  (2026-03-30T01:50:46Z)
• On the Effectiveness of LLM-Specific Fine-Tuning for Detecting AI-Generated Text [1.8428580623654867]
  This paper presents a study of AI-generated text detection based on large-scale corpora and novel training strategies.<n>We introduce a 1-billion-token corpus of human-authored texts spanning multiple genres and a 1.9-billion-token corpus of AI-generated texts.<n>Our best fine-tuned detector achieves up to $99.6%$ token-level accuracy, substantially outperforming existing open-source baselines.
  arXiv  Detail & Related papers  (2026-01-27T19:22:38Z)
• dMLLM-TTS: Self-Verified and Efficient Test-Time Scaling for Diffusion Multi-Modal Large Language Models [40.03969764207708]
  Diffusion Multi-modal Large Language Models (dMLLMs) have recently emerged as a novel architecture unifying image generation and understanding.<n>We propose dMLLM-TTS, a novel framework operating on two complementary scaling axes to unlock their full generative potential.<n>Our framework substantially improves generation quality while achieving up to 6x greater efficiency than linear search.
  arXiv  Detail & Related papers  (2025-12-22T14:31:58Z)
• DetectAnyLLM: Towards Generalizable and Robust Detection of Machine-Generated Text Across Domains and Models [60.713908578319256]
  We propose Direct Discrepancy Learning (DDL) to optimize the detector with task-oriented knowledge.<n>Built upon this, we introduce DetectAnyLLM, a unified detection framework that achieves state-of-the-art MGTD performance.<n>MIRAGE samples human-written texts from 10 corpora across 5 text-domains, which are then re-generated or revised using 17 cutting-edge LLMs.
  arXiv  Detail & Related papers  (2025-09-15T10:59:57Z)
• Towards General Visual-Linguistic Face Forgery Detection(V2) [90.6600794602029]
  Face manipulation techniques have achieved significant advances, presenting serious challenges to security and social trust.<n>Recent works demonstrate that leveraging multimodal models can enhance the generalization and interpretability of face forgery detection.<n>We propose Face Forgery Text Generator (FFTG), a novel annotation pipeline that generates accurate text descriptions by leveraging forgery masks for initial region and type identification.
  arXiv  Detail & Related papers  (2025-02-28T04:15:36Z)
• GigaCheck: Detecting LLM-generated Content [72.27323884094953]
  In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize AI-generated intervals within text.
  arXiv  Detail & Related papers  (2024-10-31T08:30:55Z)
• Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework [9.976099891796784]
  Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts. We propose a novel Multi-level Fine-grained Detection framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features.
  arXiv  Detail & Related papers  (2024-10-18T07:25:00Z)
• Training-free LLM-generated Text Detection by Mining Token Probability Sequences [18.955509967889782]
  Large language models (LLMs) have demonstrated remarkable capabilities in generating high-quality texts across diverse domains. Training-free methods, which focus on inherent discrepancies through carefully designed statistical features, offer improved generalization and interpretability. We introduce a novel training-free detector, termed textbfLastde that synergizes local and global statistics for enhanced detection.
  arXiv  Detail & Related papers  (2024-10-08T14:23:45Z)
• Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT [9.682499180341273]
  Large language models (LLMs) have significantly advanced text generation, but the human-like quality of their outputs presents major challenges.<n>We propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English.<n>This framework supports scalable, reproducible experiments and enables analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• Vietnamese AI Generated Text Detection [0.0]
  We present a dataset named ViDetect, comprising 6.800 samples of Vietnamese essay, with 3.400 samples authored by humans and the remainder generated by AI. We conducted evaluations using state-of-the-art methods, including ViT5, BartPho, PhoBERT, mDeberta V3, and mBERT. This research lays the foundation for future advancements in AI-generated text detection and provides valuable insights for researchers in the field of natural language processing.
  arXiv  Detail & Related papers  (2024-05-06T07:12:22Z)
• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text [98.28130949052313]
  A score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. We propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data.
  arXiv  Detail & Related papers  (2024-01-22T16:09:47Z)
• DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text [82.5469544192645]
  We propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT) By analyzing the differences between the original and new remaining parts through N-gram analysis, we unveil significant discrepancies between the distribution of machine-generated text and human-written text. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text.
  arXiv  Detail & Related papers  (2023-05-27T03:58:29Z)

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.