Multilingual Plagiarism Detection Using GNNs and Syntax-Semantic Knowledge Graphs

LLM-based Multilingual Fake News Detection using Cross-Lingual Semantic Alignment and Contextual Reasoning

The production of misinformation on digital platforms has been a concern in recent years, especially in interlingual contexts – people who create content in different languages and language elements that are integrated into written content.the existing fake news detectors use mostly only one language, and they are not adequately cross-linguistically generalizable in using the valuable detectors especially with low-resource languages. To overcome these weaknesses, the present paper suggests an LLM-based multilingual fake news detector which combines cross-lingual semantic alignment and contextual reasoning. The proposed approach involves the fine-tuned multilingual language model, which involves a transformer to derive contextual embeddings of multilingual text. The semantic alignment process (contrastive) is used to project the representation of the various languages to a common embedding space, and hence the transfer of knowledge. Moreover, semantic inconsistencies and misguiding tendency contribute to the reasoning of the model deducing abilities applied in the presence of attention processes. Experiments with the mixed English, Hindi, and Tamil data reveal that the proposed model leads to the accuracy of 94.3 and the F1-score of 93.6, which is better than a baseline model, including SVM, LSTM, and mBERT. The results enable mentioning the great overallization possibility and low-resource, multilingual applicability of the model. The paper gives a scaled and efficient approach to tackling the real-life aspect of multilingual fake news detection.

Arabic Plagiarism Detection Using Word2Vec-Based Semantic Features and Random Forest Classification on the ExAraPlagDet Dataset

The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.

Leveraging LLMs to Automatically Construct WordNets as Bilingual Resources

Tokenizing Crosslingual Homographs

This work proposes a simple tokenizer-level intervention based on language cues: language-specific characters replacing initial characters of shared-vocabulary words, reducing common identity during vocabulary construction, and suggests that adding lightweight language information at the tokenizer level is a promising direction for further exploration.

MORE: A Multilingual Document Parsing Benchmark and Evaluation

Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource languages like English and Chinese, creating an evaluation blind spot concerning model performance on other languages. While recent Vision-Language Models (VLMs) claim support for hundreds of languages, the lack of ground truth makes it impossible to empirically verify these capabilities. To bridge this gap, we introduce MORE, a large-scale benchmark designed for multilingual document parsing evaluation. MORE distinguishes itself through three key dimensions: (1) Unprecedented Scale: It covers 149 languages, making it the most linguistically diverse benchmark to date; (2) Structural Complexity: Unlike previous works, it extends evaluation beyond plain text to include structural elements such as code blocks, tables, and catalogs; and (3) Data Authenticity: All samples are curated from real-world documents via a model-assisted, human-refined annotation pipeline. We evaluate state-of-the-art models using MORE, establishing new performance baselines for long-tail languages and validating the benchmark's effectiveness in diagnosing model capabilities in realistic, diverse scenarios. The MORE dataset will be available at https://github.com/zimoqingfeng/MORE.

Hybrid Multilingual Transformer and Linguistic Feature Fusion for Apology Sincerity Detection

Identifying the sincerity of apologies in multilingual and code-switched environments is a difficult task due to the challenges posed by varying languages and cultures and the implicit nature of pragmatic markers of sincerity. In this paper, we propose a hybrid model based on XLM-RoBERTa contextual embeddings augmented with manually designed linguistic features encoding lexico-syntactic, pragmatic, and contextual clues. Our solution is validated on a collection of 5,190 apology expressions in English, Hindi and Hinglish collected using a 3-phase annotation framework based on crowdsourced human annotators and further validated using Claude – 4.6 (Cohen’s κ = 0.75). Using a two-pipeline framework, transformer-based semantic embeddings are combined with 21 manually engineered features to achieve better performance and explainability. Our experiments show that our proposed method can detect apologies’ sincerity with a weighted F1-score of 0.916, significantly outperforming the context-based XLM-RoBERTa model (F1 = 0.823) by 9.3% and the mBERT baseline model (F1 = 0.720) by 27.19%. Moreover, our model achieves high accuracy across different languages in a zero-shot setting with an F1-score ranging from 82.45% (English → Hindi) to 81.73% (English → Spanish). Some of the applications of this method are automated dispute resolution for e-commerce websites (for example, identifying insincere apologies from the vendors), calibrating mental health bots, and moderating social media websites that use multiple languages.