{"type":"ScholarlyArticle","title":"Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights","authors":[{"name":"Zijie Zeng","slug":"zijie-zeng","given":"Zijie","family":"Zeng","affiliation":"Centre for Learning Analytics , Monash University , Australia"},{"name":"Shiqi Liu","slug":"shiqi-liu","given":"Shiqi","family":"Liu","affiliation":"Centre for Learning Analytics , Monash University , Australia; National Engineering Research Center of Educational Big Data , Central China Normal University , China"},{"name":"Lele Sha","slug":"lele-sha","given":"Lele","family":"Sha","affiliation":"Centre for Learning Analytics , Monash University , Australia"},{"name":"Zhuang Li","slug":"zhuang-li","given":"Zhuang","family":"Li","affiliation":"Centre for Learning Analytics , Monash University , Australia"},{"name":"Kaixun Yang","slug":"kaixun-yang","given":"Kaixun","family":"Yang","affiliation":"Centre for Learning Analytics , Monash University , Australia"},{"name":"Sannyuya Liu","slug":"sannyuya-liu","given":"Sannyuya","family":"Liu","affiliation":"National Engineering Research Center of Educational Big Data , Central China Normal University , China"},{"name":"Dragan Gasevic","slug":"dragan-gasevic","given":"Dragan","family":"Gasevic","affiliation":"Centre for Learning Analytics , Monash University , Australia"},{"name":"Guangliang Chen","slug":"guangliang-chen","given":"Guangliang","family":"Chen","affiliation":"Centre for Learning Analytics , Monash University , Australia"}],"abstract":"This study explores the challenge of sentence-level AI-generated text detection within human-AI collaborative hybrid texts (abbreviated as hybrid texts). Existing studies of AI-generated text detection for hybrid texts often rely on synthetic datasets. These typically involve hybrid texts with a limited number of boundaries, e.g., single-boundary hybrid texts that begin with human-written content and end with machine-generated continuations. We contend that studies of detecting AI-generated content within hybrid texts should cover different types of hybrid texts generated in realistic settings to better inform real-world applications. Therefore, our study utilizes the CoAuthor dataset, which includes diverse, realistic hybrid texts generated through the collaboration between human writers and an intelligent writing system in multi-turn interactions. We adopt a two-step, segmentation-based pipeline: (i) detect segments within a given hybrid text where each segment contains sentences of consistent authorship, and (ii) classify the authorship of each identified segment. Our empirical findings highlight (1) detecting AI-generated sentences in hybrid texts is overall a challenging task because (1.1) human writers' selecting and even editing AI-generated sentences based on personal preferences adds difficulty in identifying the authorship of segments; (1.2) the frequent change of authorship between neighboring sentences within the hybrid text creates difficulties for segment detectors in identifying authorship-consistent segments; (1.3) the short length of text segments within hybrid texts provides limited stylistic cues for reliable authorship determination; (2) before embarking on the detection process, it is beneficial to assess the average length of segments within the hybrid text. This assessment aids in deciding whether (2.1) to employ a text segmentation-based strategy for hybrid texts with longer segments, or (2.2) to adopt a direct sentence-by-sentence classification strategy for those with shorter segments.","doi":"10.24963/ijcai.2024/835","url":"https://ijcai.org/proceedings/2024/835","pdf":"https://ijcai.org/proceedings/2024/0835.pdf","markdown":"https://ijcai.org/proceedings/2024/835.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/835","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":835,"pages":{"first":7545,"last":7553},"keywords":["Natural Language Processing: General","Humans and AI: General"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2023] Haitao Bai, Pinghui Wang, Ruofei Zhang, and Zhou Su. 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