The number of Language Models (LMs) dedicated to processing scientific text is on the rise.
Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task …

This article elucidates the concept of large model technology, summarizes the research
status of large model technology both domestically and internationally, provides an overview …

Transformer-based language models such as Bidirectional Encoder Representations from
Transformers (BERT) are now mainstream in the NLP field, but extensions to languages …

For several decades, sentiment analysis has been considered a key indicator for assessing
market mood and predicting future price changes. Accurately predicting commodity markets …

Abstract Recent advances in Technical Language Processing (TLP) have created
opportunities to leverage the rich textual data to develop new applications in data-driven …

Recent advancements in Natural Language Processing (NLP) have highlighted the success
of transformer-based models in various tasks, including Named Entity Recognition (NER) …

Significant advances have been made in natural language processing in recent years.
However, our current deep learning approach to language modeling requires substantial …

In specialized domains, the performance of generic language models can be suboptimal
due to significant domain-specific differences. To address such a problem, different pre …

Domain-adaptive pre-training (DAPT) is a technique in natural language processing (NLP)
that tailors pre-trained language models to specific domains, enhancing their performance in …