1 citations · 2 across the 5 of their papers we have counts for
5 papers
ExFold: Unified Expert Folding for Training-Free MoE Prefill-Decode Acceleration
Juntong Wu, Yifei Liu, Junyi Chen +6
Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-latency MoE serving is increasin…
Pair-In, Pair-Out: Latent Multi-Token Prediction for Efficient LLMs
Wenhui Tan, Minghao Li, Xiaoqian Ma +5
Long chain-of-thought reasoning has made autoregressive decoding the dominant inference cost of modern large language models. Existing methods target either the input side (latent…
Exploiting Contextual Knowledge in LLMs through V-usable Information based Layer Enhancement
Xiaowei Yuan, Zhao Yang, Ziyang Huang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, yet they often struggle with context-faithfulness generations that properly reflect context…
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
Yiming Ju, Ziyi Ni, Xingrun Xing +4
Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to signif…
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Xingrun Xing, Zheng Zhang, Ziyi Ni +6
Towards energy-efficient artificial intelligence similar to the human brain, the bio-inspired spiking neural networks (SNNs) have advantages of biological plausibility, event-drive…