Human-AI collaborative writing has been greatly facilitated with the help of
modern large language models (LLM), e.g., ChatGPT. While admitting the
convenience brought by technology advancement, educators also have concerns
that students might leverage LLM to partially complete their writing assignment
and pass off the human-AI hybrid text as their original work. Driven by such
concerns, in this study, we investigated the automatic detection of Human-AI
hybrid text in education, where we formalized the hybrid text detection as a
boundary detection problem, i.e., identifying the transition points between
human-written content and AI-generated content. We constructed a hybrid essay
dataset by partially removing sentences from the original student-written
essays and then instructing ChatGPT to fill in for the incomplete essays. Then
we proposed a two-step detection approach where we (1) Separated AI-generated
content from human-written content during the embedding learning process; and
(2) Calculated the distances between every two adjacent prototypes (a prototype
is the mean of a set of consecutive sentences from the hybrid text in the
embedding space) and assumed that the boundaries exist between the two
prototypes that have the furthest distance from each other. Through extensive
experiments, we summarized the following main findings: (1) The proposed
approach consistently outperformed the baseline methods across different
experiment settings; (2) The embedding learning process (i.e., step 1) can
significantly boost the performance of the proposed approach; (3) When
detecting boundaries for single-boundary hybrid essays, the performance of the
proposed approach could be enhanced by adopting a relatively large prototype
size, leading to a $22$\% improvement (against the second-best baseline method)
in the in-domain setting and an $18$\% improvement in the out-of-domain
setting.

研究了人工智能和人类协同写作中的混合文本检测问题，提出了一种基于嵌入学习过程和原型大小的两步检测方法，实验证明该方法在不同实验环境下优于基准方法，并且在单边界混合文章的边界检测中，采用较大的原型大小能够显著提高性能。

教育中人工智能混合论文自动边界检测的探索

Towards Automatic Boundary Detection for Human-AI Hybrid Essay in  Education

Learning graph representations via low-dimensional embeddings that preserve
relevant network properties is an important class of problems in machine
learning. We here present a novel method to embed directed acyclic graphs.
Following prior work, we first advocate for using hyperbolic spaces which
provably model tree-like structures better than Euclidean geometry. Second, we
view hierarchical relations as partial orders defined using a family of nested
geodesically convex cones. We prove that these entailment cones admit an
optimal shape with a closed form expression both in the Euclidean and
hyperbolic spaces, and they canonically define the embedding learning process.
Experiments show significant improvements of our method over strong recent
baselines both in terms of representational capacity and generalization.

在机器学习中，通过保留相关网络属性的低维嵌入学习图表示是一类重要的问题。本文提出了一种嵌入有向无环图的新方法，使用证明能够更好地模拟树状结构的双曲空间，并使用一组嵌套的测地凸锥来定义分层关系，并证明这些蕴含锥体在欧几里得和双曲空间中均具有一种优化的形式，而且它们可以规范地定义嵌入学习过程。实验显示，我们的方法在表示能力和泛化方面都比最近的强有力的基线有显着的改进。