18 citations · 55 across the 17 of their papers we have counts for
6 papers · 1 filter
Rethinking Document-Level Relation Extraction: A Reality Check
Jing Li, Yequan Wang, Shuai Zhang +1
Recently, numerous efforts have continued to push up performance boundaries of document-level relation extraction (DocRE) and have claimed significant progress in DocRE. In this pa…
FreeLM: Fine-Tuning-Free Language Model
Xiang Li, Xin Jiang, Xuying Meng +2
Pre-trained language models (PLMs) have achieved remarkable success in NLP tasks. Despite the great success, mainstream solutions largely follow the pre-training then finetuning pa…
Masked Structural Growth for 2x Faster Language Model Pre-training
Yiqun Yao, Zheng Zhang, Jing Li +1
Accelerating large language model pre-training is a critical issue in present research. In this paper, we focus on speeding up pre-training by progressively growing from a small Tr…
NetGPT: Generative Pretrained Transformer for Network Traffic
Xuying Meng, Chungang Lin, Yequan Wang +1
All data on the Internet are transferred by network traffic, thus accurately modeling network traffic can help improve network services quality and protect data privacy. Pretrained…
DialogPaint: A Dialog-based Image Editing Model
Jingxuan Wei, Shiyu Wu, Xin Jiang +1
We introduce DialogPaint, a novel framework that bridges conversational interactions with image editing, enabling users to modify images through natural dialogue. By integrating a…
GCRE-GPT: A Generative Model for Comparative Relation Extraction
Yequan Wang, Hengran Zhang, Aixin Sun +1
Given comparative text, comparative relation extraction aims to extract two targets (\eg two cameras) in comparison and the aspect they are compared for (\eg image quality). The ex…