This article presents a comprehensive and practical guide for practitioners and end-users
working with Large Language Models (LLMs) in their downstream Natural Language …

Understanding in-context learning (ICL) capability that enables large language models
(LLMs) to excel in proficiency through demonstration examples is of utmost importance. This …

The rapid progress in Large Language Models (LLMs) could transform many fields, but their
fast development creates significant challenges for oversight, ethical creation, and building …

In the field of natural language processing, the prevalent approach involves fine-tuning
pretrained language models (PLMs) using local samples. Recent research has exposed the …

In this perspective paper, we first comprehensively review existing evaluations of Large
Language Models (LLMs) using both standardized tests and ability-oriented benchmarks …

Large language models (LLMs) have significantly transformed the landscape of Natural
Language Processing (NLP). Their impact extends across a diverse spectrum of tasks …

In digital circuit design, testbenches (TBs) constitute the cornerstone of simulation-based
hardware verification. Traditional methodologies for testbench generation during simulation …

Large Language Models (LLMs), such as ChatGPT, have drawn a lot of attentions recently in
the legal domain due to its emergent ability to tackle a variety of legal tasks. However, it is …

Shortcut learning in deep learning models occurs when unintended features are prioritized,
resulting in degenerated feature representations and reduced generalizability and …

Language models (LMs), despite their advances, often depend on spurious correlations,
undermining their accuracy and generalizability. This study addresses the overlooked …