Variation is the Key: A Variation-Based Framework for LLM-Generated Text Detection

• URL: http://arxiv.org/abs/2602.13226v1
• Date: Tue, 27 Jan 2026 05:48:39 GMT
• Title: Variation is the Key: A Variation-Based Framework for LLM-Generated Text Detection
• Authors: Xuecong Li, Xiaohong Li, Qiang Hu, Yao Zhang, Junjie Wang,
• Abstract summary: VaryBalance is a simple but effective method for detecting text generated by large language models (LLMs)<n>The core of VaryBalance is that, compared to LLM-generated texts, there is a greater difference between human texts and their version rewritten via LLMs.<n> Comprehensive experiments demonstrated that VaryBalance outperforms the state-of-the-art detectors, i.e., Binoculars, by up to 34.3% in terms of AUROC.
• Score: 14.828776526024617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting text generated by large language models (LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rely on text-level features, leading to imprecise detection ability. In this paper, we propose a simple but effective and practical LLM-generated text detection method, VaryBalance. The core of VaryBalance is that, compared to LLM-generated texts, there is a greater difference between human texts and their rewritten version via LLMs. Leveraging this observation, VaryBalance quantifies this through mean standard deviation and distinguishes human texts and LLM-generated texts. Comprehensive experiments demonstrated that VaryBalance outperforms the state-of-the-art detectors, i.e., Binoculars, by up to 34.3\% in terms of AUROC, and maintains robustness against multiple generating models and languages.

Related papers

• RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns [50.401907401444404]
  Large language models (LLMs) are crucial for preventing misuse and building trustworthy AI systems.<n>We propose RepreGuard, an efficient statistics-based detection method.<n> Experimental results show that RepreGuard outperforms all baselines with average 94.92% AUROC on both in-distribution (ID) and OOD scenarios.
  arXiv  Detail & Related papers  (2025-08-18T17:59:15Z)
• Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders [20.557610461777344]
  We use Sparse Autoencoders (SAE) to extract features from Gemma-2-2b residual stream.<n>We identify both interpretable and efficient features, analyzing their semantics and relevance.<n>Our methods offer valuable insights into how texts from various models differ from human-written content.
  arXiv  Detail & Related papers  (2025-03-05T15:33:52Z)
• Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
  Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
  arXiv  Detail & Related papers  (2024-10-04T18:42:09Z)
• 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)
• Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore [51.65730053591696]
  We propose a simple yet effective black-box zero-shot detection approach based on the observation that human-written texts typically contain more grammatical errors than LLM-generated texts.<n> Experimental results show that our method outperforms current state-of-the-art (SOTA) zero-shot and supervised methods.
  arXiv  Detail & Related papers  (2024-05-07T12:57:01Z)
• 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)
• MAGE: Machine-generated Text Detection in the Wild [82.70561073277801]
  Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection. We build a comprehensive testbed by gathering texts from diverse human writings and texts generated by different LLMs. Despite challenges, the top-performing detector can identify 86.54% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.
  arXiv  Detail & Related papers  (2023-05-22T17:13:29Z)

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.