4 citations · 8 across the 6 of their papers we have counts for
6 papers
Lossless KV Cache Compression to 2%
Zhen Yang, J. N. Han, Kan Wu +4
Large language models have revolutionized data processing in numerous domains, with their ability to handle extended context reasoning receiving notable recognition. To speed up in…
HMoE: Heterogeneous Mixture of Experts for Language Modeling
An Wang, Xingwu Sun, Ruobing Xie +9
Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous…
Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer
Youmi Ma, An Wang, Naoaki Okazaki
Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. While studies have been conducted on English DocRE, limited attenti…
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction
Youmi Ma, An Wang, Naoaki Okazaki
Document-level relation extraction (DocRE) is the task of identifying all relations between each entity pair in a document. Evidence, defined as sentences containing clues for the…
Semi-Supervised Formality Style Transfer with Consistency Training
Ao Liu, An Wang, Naoaki Okazaki
Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. To address the data-scarcity problem of exi…
Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network
An Wang, Ao Liu, Hieu Hanh Le +1
Entity-relation extraction aims to jointly solve named entity recognition (NER) and relation extraction (RE). Recent approaches use either one-way sequential information propagatio…