The rise of large language models (LLMs) has marked a pivotal shift in the field of natural
language processing (NLP). LLMs have revolutionized a multitude of domains, and they …

The advancement of large language models (LLMs) has enhanced the ability to generalize
across a wide range of unseen natural language processing (NLP) tasks through instruction …

Norwegian, spoken by only 5 million population, is under-representative within the most
impressive breakthroughs in NLP tasks. To the best of our knowledge, there has not yet …

Despite the widespread use of the Persian language by millions globally, limited efforts have
been made in natural language processing for this language. The use of large language …

With the burgeoning development in the realm of large language models (LLMs), the
demand for efficient incremental training tailored to specific industries and domains …

Catastrophic forgetting remains a critical challenge in the field of continual learning, where
neural networks struggle to retain prior knowledge while assimilating new information. Most …

While significant progress has been made in multi-modal learning driven by large-scale
image-text datasets, there is still a noticeable gap in the availability of such datasets within …

Knowledge Distillation (KD) is a predominant approach for BERT compression. Previous KD-
based methods focus on designing extra alignment losses for the student model to mimic the …

Multimodal pre-training demonstrates its potential in the medical domain, which learns
medical visual representations from paired medical reports. However, many pre-training …

Abstract Background Assigning International Classification of Diseases (ICD) codes to
clinical texts is a common and crucial practice in patient classification, hospital management …