most citedCE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models

2 citations · 2 across the 5 of their papers we have counts for

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cs.LG2025

TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network

Guangxin He, Yuan Cao, Yutong He +4

Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participants, but is often bottlenecked by…

cs.LG2025★ 2 cited

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models

Guanduo Chen, Yutong He, Yipeng Hu +2

Large Language Models (LLMs) demonstrate exceptional performance across various tasks but demand substantial computational resources even for fine-tuning computation. Although Low-…