Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Despite the remarkable success of large-scale Language Models (LLMs) such as GPT-3,
their performances still significantly underperform fine-tuned models in the task of text …

Since ChatGPT released its API for public use, the number of applications built on top of
commercial large language models (LLMs) increase exponentially. One popular usage of …

As the reach of large language models (LMs) expands globally, their ability to cater to
diverse cultural contexts becomes crucial. Despite advancements in multilingual …

Prevalent supervised learning methods in natural language processing (NLP) are
notoriously data-hungry, which demand large amounts of high-quality annotated data. In …

Hierarchical text classification (HTC) is an important task with broad applications, and few-
shot HTC has gained increasing interest recently. While in-context learning (ICL) with large …

We present LLoVi, a language-based framework for long-range video question-answering
(LVQA). Unlike prior long-range video understanding methods, which are often costly and …

Large language models (LLMs) exhibited powerful capability in various natural language
processing tasks. This work focuses on exploring LLM performance on zero-shot information …

Large language models (LLMs) outperform information retrieval techniques for downstream
knowledge-intensive tasks when being prompted to generate world knowledge. However …