5 citations · 9 across the 7 of their papers we have counts for
7 papers
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling
Xianzhen Luo, Yixuan Wang, Qingfu Zhu +4
Massive parameters of LLMs have made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-ver…
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
Yixuan Wang, Xianzhen Luo, Fuxuan Wei +6
Existing speculative decoding methods typically require additional model structure and training processes to assist the model for draft token generation. This makes the migration o…
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Weixiang Zhao, Shilong Wang, Yulin Hu +6
The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific k…
CGCE: A Chinese Generative Chat Evaluation Benchmark for General and Financial Domains
Xuanyu Zhang, Bingbing Li, Qing Yang
Generative chat models, such as ChatGPT and GPT-4, have revolutionized natural language generation (NLG) by incorporating instructions and human feedback to achieve significant per…
XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
Xuanyu Zhang, Qing Yang, Dongliang Xu
In recent years, pre-trained language models have undergone rapid development with the emergence of large-scale models. However, there is a lack of open-sourced chat models specifi…
Self-QA: Unsupervised Knowledge Guided Language Model Alignment
Xuanyu Zhang, Qing Yang
Large-scale language models like ChatGPT and GPT-4 have gained attention for their impressive conversational and generative capabilities. However, the creation of supervised paired…
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
Xuanyu Zhang, Qing Yang, Dongliang Xu
Knowledge graph embedding (KGE) aims to learn continuous vectors of relations and entities in knowledge graph. Recently, transition-based KGE methods have achieved promising perfor…