2 citations · 3 across the 8 of their papers we have counts for
8 papers
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption
Xue Tan, Yi Zheng, Chang Huo +7
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, effectively mitigating their inherent knowledge limitations. However…
Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training
Ping Chen, Zhuohong Deng, Ping Li +6
Training large language models (LLMs) is often constrained by GPU memory limitations. To alleviate memory pressure, activation recomputation and data compression have been proposed…
Huawei Cloud Model-as-a-Service on the CloudMatrix384 SuperPod
Ao Xiao, Bangzheng He, Baoquan Zhang +125
Scaled-out MoE LLMs and scaled-up SuperPods create new systems challenges for production Model-as-a-Service (MaaS), requiring disaggregation, low-latency communication, and decentr…
FASP: Fast and Accurate Structured Pruning of Large Language Models
Hanyu Hu, Pengxiang Zhao, Ping Li +3
The rapid increase in the size of large language models (LLMs) has significantly escalated their computational and memory demands, posing challenges for efficient deployment, espec…
A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models
Pengxiang Zhao, Hanyu Hu, Ping Li +3
Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising p…
Optimizing Large Model Training through Overlapped Activation Recomputation
Ping Chen, Wenjie Zhang, Shuibing He +10
Large model training often uses recomputation to alleviate memory pressure and pipelines to exploit the parallelism of data, tensors, and devices. However, existing recomputation a…
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching
Dong Yao
Sentence semantic matching is a research hotspot in natural language processing, which is considerably significant in various key scenarios, such as community question answering, s…
Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices
Pengxiang Zhao, Ping Li, Yingjie Gu +4
As deep learning models exponentially increase in size, optimizers such as Adam encounter significant memory consumption challenges due to the storage of first and second moment da…