用多头模型识别科学论文中的AI生成片段，效果比基线提升9%。
Multi-head Span-based Detector for AI-generated Fragments in Scientific Papers
- 设计双头多任务模型，利用连续文本段落特征检测AI生成内容。
- 在开发集上平均宏F1提升至0.95，闭测试集达0.96。
- 适合关注学术文本真实性与AI检测的科研人员使用。
本文介绍了一种用于区分科学文档中人工智能生成与人类撰写片段的系统，参与了第四届科学文档处理研讨会举办的DAGPap24竞赛。该任务旨在识别科学领域文档中以词元级别存在的生成文本片段。研究聚焦于采用具有两个输出头的多任务学习架构，其设计基于任务特性——目标文本片段在数百字符范围内连续存在。通过对比不同编码器变体获取序列中每个词元的状态向量，并调整片段分词方式输入基于Transformer的编码器，实现了显著性能提升。在开发集上，平均宏F1-score从基线0.86提高至0.95，较基线提升9%；在竞赛闭测试集上达到0.96的得分。
原文摘要 · Abstract (English)
This paper describes a system designed to distinguish between AI-generated and human-written scientific excerpts in the DAGPap24 competition hosted within the Fourth Workshop on Scientific Document Processing. In this competition the task is to find artificially generated token-level text fragments in documents of a scientific domain. Our work focuses on the use of a multi-task learning architecture with two heads. The application of this approach is justified by the specificity of the task, where class spans are continuous over several hundred characters. We considered different encoder variations to obtain a state vector for each token in the sequence, as well as a variation in splitting fragments into tokens to further feed into the input of a transform-based encoder. This approach allows us to achieve a 9% quality improvement relative to the baseline solution score on the development set (from 0.86 to 0.95) using the average macro F1-score, as well as a score of 0.96 on a closed test part of the dataset from the competition.
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