Multimodal named entity recognition and relation extraction (MNER and MRE) is a
fundamental and crucial branch in information extraction. However, existing approaches for …

Using prompts to utilize language models to perform various downstream tasks, also known
as prompt-based learning or prompt-learning, has lately gained significant success in …

The objective of few-shot named entity recognition is to identify named entities with limited
labeled instances. Previous works have primarily focused on optimizing the traditional token …

Knowledge editing for large language models can offer an efficient solution to alter a
model's behavior without negatively impacting the overall performance. However, the …

Prompt-tuning based few-shot learning has garnered increasing attention in recent years
due to its efficiency and promising capability. To achieve the best performance for NLP tasks …

Recently, prompt-based generative frameworks have shown impressive capabilities in
sequence labeling tasks. However, in practical dialogue scenarios, relying solely on …

This article introduces contrastive alignment instructions (AlignInstruct) to address two
challenges in machine translation (MT) on large language models (LLMs). One is the …

Demonstration learning aims to guide the prompt prediction via providing answered
demonstrations in the few shot settings. Despite achieving promising results, existing work …

Generative large language models (LLMs), such as GPT-4, have demonstrated remarkable
performance across a wide range of NLP tasks. The increased number of LLMs' parameters …

Event causality identification (ECI) is a critical and challenging information extraction task,
which aims to identify whether there is a causal relationship between the two events. To …