12 citations · 34 across the 14 of their papers we have counts for
9 papers · 1 filter
Order Matters: Rethinking Prompt Construction in In-Context Learning
Warren Li, Yiqian Wang, Zihan Wang +1
In-context learning (ICL) enables large language models to perform new tasks by conditioning on a sequence of examples. Most prior work reasonably and intuitively assumes that whic…
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
Letian Peng, Zilong Wang, Feng Yao +2
Information extraction (IE) is a fundamental area in natural language processing where prompting large language models (LLMs), even with in-context examples, cannot defeat small LM…
DOCMASTER: A Unified Platform for Annotation, Training, & Inference in Document Question-Answering
Alex Nguyen, Zilong Wang, Jingbo Shang +1
The application of natural language processing models to PDF documents is pivotal for various business applications yet the challenge of training models for this purpose persists i…
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
Letian Peng, Yuwei Zhang, Zilong Wang +4
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embedding…
MEMORYLLM: Towards Self-Updatable Large Language Models
Yu Wang, Yifan Gao, Xiusi Chen +9
Existing Large Language Models (LLMs) usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a c…
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
Zilong Wang, Hao Zhang, Chun-Liang Li +8
Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verificat…
Less than One-shot: Named Entity Recognition via Extremely Weak Supervision
Letian Peng, Zihan Wang, Jingbo Shang
We study the named entity recognition (NER) problem under the extremely weak supervision (XWS) setting, where only one example entity per type is given in a context-free way. While…
EmojiLM: Modeling the New Emoji Language
Letian Peng, Zilong Wang, Hang Liu +2
With the rapid development of the internet, online social media welcomes people with different backgrounds through its diverse content. The increasing usage of emoji becomes a noti…
Fast-ELECTRA for Efficient Pre-training
Chengyu Dong, Liyuan Liu, Hao Cheng +3
ELECTRA pre-trains language models by detecting tokens in a sequence that have been replaced by an auxiliary model. Although ELECTRA offers a significant boost in efficiency, its p…