DeformAr: Rethinking NER Evaluation through Component Analysis and Visual Analytics

Large language models (LLMs) have shown significant potential in various language-related tasks, yet their ability to grasp deeper linguistic properties such as syntax, phonetics, and metaphor remains under investigation. A new multilingual genre classification dataset has been introduced, derived from Project Gutenberg, to assess LLMs' effectiveness in learning and applying these features across six languages: English, French, German, Italian, Spanish, and Portuguese.

The Model-agnostic Saliency Estimation (MASE) framework has been introduced to enhance the interpretability of deep neural networks (DNNs) in Natural Language Processing (NLP). MASE provides local explanations for text-based predictive models by utilizing Normalized Linear Gaussian Perturbations (NLGP) on the embedding layer, thus avoiding the limitations of traditional post-hoc interpretation methods.

Large Language Models (LLMs) are being explored for their zero-shot multilingual capabilities, particularly in the context of information retrieval from Electronic Health Records (EHRs) in Italian healthcare. This research highlights the potential of LLMs to enhance the extraction of critical information from complex clinical texts, addressing limitations of traditional NLP methods.

Large language models (LLMs) have shown significant advancements in natural language processing (NLP), yet challenges remain in achieving deeper semantic understanding and contextual coherence. Recent research discusses methodologies to enhance LLMs through advanced natural language understanding techniques, including semantic parsing and knowledge integration.

A new paper on arXiv presents datasets aimed at improving the understanding of idiomatic and figurative language in Natural Language Processing (NLP). These datasets are designed to assist large language models (LLMs) in better interpreting informal language, which has become increasingly prevalent in social media and everyday communication.

Recent research highlights the persistent bias in multilingual large language models (LLMs) towards Islam, revealing that these models often misrepresent religious contexts, particularly when responding in Bengali compared to English. The study introduces the BRAND dataset, which focuses on major South Asian religions and aims to improve bias detection in AI systems.

Recent research has shown that large language models (LLMs) can effectively differentiate between grammatical and ungrammatical sentences, revealing that various types of syntactic agreement, such as subject-verb and determiner-noun, utilize overlapping units within these models. This study involved a functional localization approach to identify the responsive units across 67 English syntactic phenomena in seven open-weight models.

A new dataset named Reveal-Bangla has been introduced, focusing on cross-lingual multi-step reasoning evaluation in Bangla, derived from the English Reveal dataset. This dataset includes both binary and non-binary question types and aims to assess the reasoning capabilities of multilingual small language models in Bangla compared to English.