{"type":"ScholarlyArticle","title":"VaryBalance: Detecting LLM-Generated Text Through Variation","authors":[{"name":"Xuecong Li","slug":"xuecong-li","given":"Xuecong","family":"Li","affiliation":""},{"name":"Xiaohong Li","slug":"xiaohong-li","given":"Xiaohong","family":"Li","affiliation":""},{"name":"Qiang Hu","slug":"qiang-hu","given":"Qiang","family":"Hu","affiliation":""},{"name":"Yao Zhang","slug":"yao-zhang","given":"Yao","family":"Zhang","affiliation":""},{"name":"Junjie Wang","slug":"junjie-wang","given":"Junjie","family":"Wang","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/648","url":"https://ijcai.org/proceedings/2026/648","pdf":"https://ijcai.org/proceedings/2026/0648.pdf","markdown":"https://ijcai.org/proceedings/2026/648.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/648","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Natural Language Processing","track":"Main Track","number":648,"pages":{"first":5819,"last":5827},"keywords":["Natural Language Processing: Language models","Natural Language Processing: Text classification","Natural Language Processing: Interpretability and analysis of models for NLP"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}