Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

Transfer learning is a technique utilized in deep learning applications to transmit learned
inference to a different target domain. The approach is mainly to solve the problem of a few …

Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies
concentrate on fully pre-trained LLMs to better understand and improve LLMs' …

Deep learning has recently come to dominate computational linguistics, leading to claims of
human-level performance in a range of language processing tasks. Like much previous …

Given the prevalence of pre-trained contextualized representations in today's NLP, there
have been many efforts to understand what information they contain, and why they seem to …

Transfer learning from pre-trained neural language models towards downstream tasks has
been a predominant theme in NLP recently. Several researchers have shown that deep NLP …

The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

To evaluate knowledge in large language models (LLMs), current methods query the model
and then evaluate its generated responses. In this work, we ask whether evaluation can be …

Transformer-based language models such as BERT and its variants have found widespread
use in natural language processing (NLP). A common way of using these models is to fine …