Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

This article surveys and organizes research works in a new paradigm in natural language
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …

This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …

Prompt tuning, which only tunes continuous prompts with a frozen language model,
substantially reduces per-task storage and memory usage at training. However, in the …

Prompting a pretrained language model with natural language patterns has been proved
effective for natural language understanding (NLU). However, our preliminary study reveals …

Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-
trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved …

Tuning pre-trained language models (PLMs) with task-specific prompts has been a
promising approach for text classification. Particularly, previous studies suggest that prompt …

As Natural Language Processing (NLP) systems are increasingly employed in intricate
social environments, a pressing query emerges: Can these NLP systems mirror human …

The contemporary LLMs are prone to producing hallucinations, stemming mainly from the
knowledge gaps within the models. To address this critical limitation, researchers employ …

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 …