Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

The rapid progress in conversational AI has given rise to advanced language models
capable of generating human-like texts. Among these models, ChatGPT and Bard …

Currently, most applications in the Industrial Internet of Things (IIoT) still rely on CNN-based
neural networks. Although Transformer-based large models (LMs), including language …

The recent success of LLMs has been predominantly driven by curating the training dataset
composition, scaling of model architectures and dataset sizes and advancements in …

Dense multi-representation retrieval models, exemplified as ColBERT, estimate the
relevance between a query and a document based on the similarity of their contextualised …

Abstract Language Models (LM) are becoming more and more useful for providing
representations upon which to train Natural Language Processing applications. However …

Pretrained language models augmented with in-domain corpora show impressive results in
biomedicine and clinical Natural Language Processing (NLP) tasks in English. However …

Training of a tokenizer plays an important role in the performance of deep learning models.
This research aims to understand the performance of tokenizers in five state-of-the-art …

The target task of our team in CLEF2022 CheckThat! Lab challenge is Task-1C, harmful
tweet detection. We propose a novel approach, called ARC-NLP-contra, which is a …

In recent years, major advancements in natural language processing (NLP) have been
driven by the emergence of large language models (LLMs), which have significantly …