118 citations · 199 across the 20 of their papers we have counts for
21 papers
Label-Centric Knowledge Retrieval for Zero-Shot Text Classification
Daoping Wu, Dong Wang, Siyu Liu +5
Flagship large language models (LLMs) offer rich general knowledge and strong reasoning capabilities, but their high inference cost and latency limit their use for large-scale data…
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
NorMuon: Making Muon more efficient and scalable
Zichong Li, Liming Liu, Chen Liang +2
The choice of optimizer significantly impacts the training efficiency and computational costs of large language models (LLMs). Recently, the Muon optimizer has demonstrated promisi…
LLMs Can Generate a Better Answer by Aggregating Their Own Responses
Zichong Li, Xinyu Feng, Yuheng Cai +6
Large Language Models (LLMs) have shown remarkable capabilities across tasks, yet they often require additional prompting techniques when facing complex problems. While approaches…
COSMOS: A Hybrid Adaptive Optimizer for Memory-Efficient Training of LLMs
Liming Liu, Zhenghao Xu, Zixuan Zhang +5
Large Language Models (LLMs) have demonstrated remarkable success across various domains, yet their optimization remains a significant challenge due to the complex and high-dimensi…
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Yixiao Li, Yifan Yu, Chen Liang +4
Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario wher…