most citedScaling Sentence Embeddings with Large Language Models

9 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20243 cited

Improving Domain Adaptation through Extended-Text Reading Comprehension

Ting Jiang, Shaohan Huang, Shengyue Luo +8

To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method. Recent work demonstrates that adapti…

eess.SP2023

FrFT based estimation of linear and nonlinear impairments using Vision Transformer

Ting Jiang, Zheng Gao, Yizhao Chen +2

To comprehensively assess optical fiber communication system conditions, it is essential to implement joint estimation of the following four critical impairments: nonlinear signal-…

cs.CL20239 cited

Scaling Sentence Embeddings with Large Language Models

Ting Jiang, Shaohan Huang, Zhongzhi Luan +2

Large language models (LLMs) have recently garnered significant interest. With in-context learning, LLMs achieve impressive results in various natural language tasks. However, the…

cs.DL2023

Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

Hao Geng, Deqing Wang, Fuzhen Zhuang +5

Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are prop…

cs.CV20233 cited

DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution

Lei Yu, Xinpeng Li, Youwei Li +4

Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly fo…