5 papers
D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models
Liyuan Zhang, Jiarui Zhang, Jinwei Yao +6
Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model forward pass. Recent diffusio…
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…
Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
Fangyu Ding, Ding Ding, Sijin Chen +8
While Masked Diffusion Language Models (MDLMs) relying on token masking and unmasking have shown promise in language modeling, their computational efficiency and generation flexibi…
Prompt Inversion Attack against Collaborative Inference of Large Language Models
Wenjie Qu, Yuguang Zhou, Yongji Wu +4
Large language models (LLMs) have been widely applied for their remarkable capability of content generation. However, the practical use of open-source LLMs is hindered by high reso…
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-…