Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

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 …

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the
development of numerous cost-effective and accessible alternatives that are created by …

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 …

Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-
trained vision models. However, with the exponential growth of model sizes, the …

Facial affective behavior analysis (FABA) is crucial for understanding human mental states
from images. However, traditional approaches primarily deploy models to discriminate …

Purpose This study aims to adopt a systematic review approach to examine the existing
literature on law and LLMs. It involves analyzing and synthesizing relevant research papers …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Fine-tuning and inference with large Language Models (LM) are generally known to be
expensive. Parameter-efficient fine-tuning over pretrained LMs reduces training memory by …

This technical report briefly describes our JDExplore d-team's Vega v2 submission on the
SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general …