This paper describes AIpom, a system designed to detect a boundary between
human-written and machine-generated text (SemEval-2024 Task 8, Subtask C:
Human-Machine Mixed Text Detection). We propose a two-stage pipeline combining
predictions from an instruction-tuned decoder-only model and encoder-only
sequence taggers. AIpom is ranked second on the leaderboard while achieving a
Mean Absolute Error of 15.94. Ablation studies confirm the benefits of
pipelining encoder and decoder models, particularly in terms of improved
performance.

AIpom 是一个系统，用于检测人工编写和机器生成文本之间的边界，并通过使用经过指令调整的解码器模型和仅编码器模型的序列标记器来提出一个两阶段的流水线。AIpom 在排行榜上排名第二，平均绝对误差达到 15.94，混合了人工编写和机器生成文本的检测。消融研究证实了编码器和解码器模型流水线的益处，特别是在性能提升方面。

AIpom 参加 SemEval-2024 任务 8：在 M4 中检测 AI 产生的输出

AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4

The potential of artificial intelligence (AI)-based large language models
(LLMs) holds considerable promise in revolutionizing education, research, and
practice. However, distinguishing between human-written and AI-generated text
has become a significant task. This paper presents a comparative study,
introducing a novel dataset of human-written and LLM-generated texts in
different genres: essays, stories, poetry, and Python code. We employ several
machine learning models to classify the texts. Results demonstrate the efficacy
of these models in discerning between human and AI-generated text, despite the
dataset's limited sample size. However, the task becomes more challenging when
classifying GPT-generated text, particularly in story writing. The results
indicate that the models exhibit superior performance in binary classification
tasks, such as distinguishing human-generated text from a specific LLM,
compared to the more complex multiclass tasks that involve discerning among
human-generated and multiple LLMs. Our findings provide insightful implications
for AI text detection while our dataset paves the way for future research in
this evolving area.

通过对人类写作文本和基于大型语言模型的生成文本进行比较研究，该论文展示了在不同文体中对于人类文本和生成文本进行分类的机器学习模型的效果，同时指出在故事写作方面识别生成文本的困难性，为未来在人工智能文本识别方面提供了启示和研究数据集。