Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

Probing classifiers have emerged as one of the prominent methodologies for interpreting
and analyzing deep neural network models of natural language processing. The basic idea …

Modern machine learning has subverted and bypassed the theoretical framework of
Chomsky's generative approach to linguistics, including its core claims to particular insights …

While large language models (LLMs) like GPT-3 have achieved impressive results on
multiple choice question answering (MCQA) tasks in the zero, one, and few-shot settings …

Abstract Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently
gained significant popularity for creative text-to-image generation. However for domain …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

The remarkable success of pretrained language models has motivated the study of what
kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the …

Large Language Models (LLMs) have shown extraordinary capabilities in understanding
and generating text that closely mirrors human communication. However, a primary …

There has been growing interest in parameter-efficient methods to apply pre-trained
language models to downstream tasks. Building on the Prompt Tuning approach of Lester et …

Feed-forward layers constitute two-thirds of a transformer model's parameters, yet their role
in the network remains under-explored. We show that feed-forward layers in transformer …