Large Language Models (LLMs) have showcased impressive abilities in
generating fluent responses to diverse user queries. However, concerns
regarding the potential misuse of such texts in journalism, educational, and
academic contexts have surfaced. SemEval 2024 introduces the task of
Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text
Detection, aiming to develop automated systems for identifying
machine-generated text and detecting potential misuse. In this paper, we i)
propose a RoBERTa-BiLSTM based classifier designed to classify text into two
categories: AI-generated or human ii) conduct a comparative study of our model
with baseline approaches to evaluate its effectiveness. This paper contributes
to the advancement of automatic text detection systems in addressing the
challenges posed by machine-generated text misuse. Our architecture ranked 46th
on the official leaderboard with an accuracy of 80.83 among 125.

本文提出了一种基于 RoBERTa-BiLSTM 的分类器，用于将文本分为两个类别：人工智能生成或人类生成，并与基准方法进行了兼容研究以评估其有效性，从而促进了自动文本检测系统在解决机器生成文本滥用方面的发展。其中，我们的架构在 125 个参赛者中以 80.83% 的准确率排名第 46 名。

追踪文本起源的 RoBERTa-BiLSTM 方法: Mast Kalandar 在 SemEval-2024 任务 8 中探测生成的 AI 文本

Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins:  RoBERTa-BiLSTM Approach to Detect AI-Generated Text

Effectively analyzing the comments to uncover latent intentions holds immense
value in making strategic decisions across various domains. However, several
challenges hinder the process of sentiment analysis including the lexical
diversity exhibited in comments, the presence of long dependencies within the
text, encountering unknown symbols and words, and dealing with imbalanced
datasets. Moreover, existing sentiment analysis tasks mostly leveraged
sequential models to encode the long dependent texts and it requires longer
execution time as it processes the text sequentially. In contrast, the
Transformer requires less execution time due to its parallel processing nature.
In this work, we introduce a novel hybrid deep learning model, RoBERTa-BiLSTM,
which combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) with
Bidirectional Long Short-Term Memory (BiLSTM) networks. RoBERTa is utilized to
generate meaningful word embedding vectors, while BiLSTM effectively captures
the contextual semantics of long-dependent texts. The RoBERTa-BiLSTM hybrid
model leverages the strengths of both sequential and Transformer models to
enhance performance in sentiment analysis. We conducted experiments using
datasets from IMDb, Twitter US Airline, and Sentiment140 to evaluate the
proposed model against existing state-of-the-art methods. Our experimental
findings demonstrate that the RoBERTa-BiLSTM model surpasses baseline models
(e.g., BERT, RoBERTa-base, RoBERTa-GRU, and RoBERTa-LSTM), achieving accuracies
of 80.74%, 92.36%, and 82.25% on the Twitter US Airline, IMDb, and Sentiment140
datasets, respectively. Additionally, the model achieves F1-scores of 80.73%,
92.35%, and 82.25% on the same datasets, respectively.

通过结合 RoBERTa 和 BiLSTM 网络的 RoBERTa-BiLSTM 混合深度学习模型，在情感分析中实现更好的性能。