This document contains the details of the authors' submission to the
proceedings of SemEval 2024's Task 8: Multigenerator, Multidomain, and
Multilingual Black-Box Machine-Generated Text Detection Subtask A (monolingual)
and B. Detection of machine-generated text is becoming an increasingly
important task, with the advent of large language models (LLMs). In this
document, we lay out the techniques utilized for performing the same, along
with the results obtained.

该研究论文涵盖了作者对 SemEval 2024 任务 8 的提交详细内容，即多生成器、多领域和多语言黑盒机器生成文本检测子任务 A 和 B。该论文阐述了进行同样任务所使用的技术和所得结果。

SemEval-2024 任务 8：加权层平均 RoBERTa 用于黑盒机器生成文本检测

SemEval-2024 Task 8: Weighted Layer Averaging RoBERTa for Black-Box  Machine-Generated Text Detection

SemEval-2024 Task 8 is focused on multigenerator, multidomain, and
multilingual black-box machine-generated text detection. Such a detection is
important for preventing a potential misuse of large language models (LLMs),
the newest of which are very capable in generating multilingual human-like
texts. We have coped with this task in multiple ways, utilizing language
identification and parameter-efficient fine-tuning of smaller LLMs for text
classification. We have further used the per-language classification-threshold
calibration to uniquely combine fine-tuned models predictions with statistical
detection metrics to improve generalization of the system detection
performance. Our submitted method achieved competitive results, ranking at the
fourth place, just under 1 percentage point behind the winner.

SemEval-2024 任务 8 专注于多产生器、多领域和多语言黑盒机器生成文本检测，并应用语言识别和参数高效微调小型 LLM 进行文本分类，通过每种语言分类阈值校准将微调模型预测与统计检测指标相结合，提高系统检测性能的泛化能力，我们的方案取得了竞争力的结果，在第四名，仅比冠军低 1 个百分点。