Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs
have been used to generate texts in different languages and for different
tasks. Additionally, due to the participation of remarkable companies such as
Google and OpenAI, LLMs are now more accessible, and people can easily use
them. However, an important issue is how we can detect AI-generated texts from
human-written ones. In this article, we have investigated the problem of
AI-generated text detection from two different aspects: semantics and syntax.
Finally, we presented an AI model that can distinguish AI-generated texts from
human-written ones with high accuracy on both multilingual and monolingual
tasks using the M4 dataset. According to our results, using a semantic approach
would be more helpful for detection. However, there is a lot of room for
improvement in the syntactic approach, and it would be a good approach for
future work.

研究表明，利用大型语言模型可以高准确度地区分人工智能生成的文本和人类编写的文本，尤其在语义方面会更加有帮助，而句法方面还有改进的空间。

RFBES 在 SemEval-2024 任务 8 中的应用：探索用于区分人工智能生成和人类编写的文本的句法和语义特征

RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic  Features for Distinguishing AI-Generated and Human-Written Texts

Large language models (LLMs) have demonstrated remarkable capability to
generate fluent responses to a wide variety of user queries, but this has also
resulted in concerns regarding the potential misuse of such texts in
journalism, educational, and academic context. In this work, we aim to develop
automatic systems to identify machine-generated text and to detect potential
misuse. We first introduce a large-scale benchmark M4, which is
multi-generator, multi-domain, and multi-lingual corpus for machine-generated
text detection. Using the dataset, we experiment with a number of methods and
we show that it is challenging for detectors to generalize well on unseen
examples if they are either from different domains or are generated by
different large language models. In such cases, detectors tend to misclassify
machine-generated text as human-written. These results show that the problem is
far from solved and there is a lot of room for improvement. We believe that our
dataset M4, which covers different generators, domains and languages, will
enable future research towards more robust approaches for this pressing
societal problem. The M4 dataset is available at
this https URL

本文介绍了一个大规模的文本检测数据集 M4，并利用该数据集实验了多种方法，发现在不同领域或用不同的大型语言模型生成的文本中，模型检测器倾向于将机器生成的文本误分类为人工书写的文本，并指出解决此问题仍有很多待改进的空间，M4 数据集将为以后研究提供优质的数据支持。

M4: 多发生器、多领域和多语言的黑盒机器生成文本检测

M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box  Machine-Generated Text Detection

Time series forecasting is an important problem, with many real world
applications. Ensembles of deep neural networks have recently achieved
impressive forecasting accuracy, but such large ensembles are impractical in
many real world settings. Transformer models been successfully applied to a
diverse set of challenging problems. We propose a novel adaptation of the
original Transformer architecture focusing on the task of time series
forecasting, called Persistence Initialization. The model is initialized as a
naive persistence model by using a multiplicative gating mechanism combined
with a residual skip connection. We use a decoder Transformer with ReZero
normalization and Rotary positional encodings, but the adaptation is applicable
to any auto-regressive neural network model. We evaluate our proposed
architecture on the challenging M4 dataset, achieving competitive performance
compared to ensemble based methods. We also compare against existing recently
proposed Transformer models for time series forecasting, showing superior
performance on the M4 dataset. Extensive ablation studies show that Persistence
Initialization leads to better performance and faster convergence. As the size
of the model increases, only the models with our proposed adaptation gain in
performance. We also perform an additional ablation study to determine the
importance of the choice of normalization and positional encoding, and find
both the use of Rotary encodings and ReZero normalization to be essential for
good forecasting performance.

本研究提出一种基于 Transformer 架构的时间序列预测方法，称为持续初始化。该方法在 M4 数据集上与传统集合方法相比表现良好，且当模型大小增加时，仅我们提出的这种适应性架构才能提高性能，使用旋转编码和 ReZero 归一化对于良好的预测表现则至关重要。