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 …

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP)
but demand massive GPU resources for training. Lowering the threshold for LLMs training …

Parameter-efficient fine-tuning aims to achieve performance comparable to fine-tuning,
using fewer trainable parameters. Several strategies (eg, Adapters, prefix tuning, BitFit, and …

Fine-tuning large language models for different tasks can be costly and inefficient, and even
methods that reduce the number of tuned parameters still require full gradient-based …

We develop a large language model (LLM) based automatic speech recognition (ASR)
system that can be contextualized by providing keywords as prior information in text …

We propose a training model based on BERT series. This method uses sliding window
technique to preprocess data sets to train and solve multi-author writing style analysis tasks …

Fine-tuning is a widely used technique for leveraging pre-trained language models (PLMs)
in downstream tasks, but it can be computationally expensive and storage-intensive. To …

Class incremental learning (CIL) aims to develop an open intelligence system that can
continuously learn new concepts from new tasks while retaining the knowledge to …

Large-scale pre-trained language models (PLM) are today's leading technology for a wide
range of natural language processing tasks. However, the enormous size of these models …