Large language models (LLMs) have significantly advanced the field of natural language
processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of …

With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

Recently, fine-tuning language models pre-trained on large text corpora have provided huge
improvements on vision-and-language (V&L) tasks as well as on pure language tasks …

Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the
standard method for achieving state-of-the-art performance on NLP benchmarks. However …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

There remain many open questions pertaining to the scaling behaviour of Transformer
architectures. These scaling decisions and findings can be critical, as training runs often …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Adapting large-scale pretrained models to various downstream tasks via fine-tuning is a
standard method in machine learning. Recently, parameter-efficient fine-tuning methods …

Current methods for few-shot fine-tuning of pretrained masked language models (PLMs)
require carefully engineered prompts and verbalizers for each new task to convert examples …