4 papers
Motivating Next-Gen Accelerators with Flexible (N:M) Activation Sparsity via Benchmarking Lightweight Post-Training Sparsification Approaches
Shirin Alanova, Kristina Kazistova, Ekaterina Galaeva +7
The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N:M) pruning is well-established for we…
Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning
Shiwan Zhao, Zhihu Wang, Xuyang Zhao +10
Post-training has become central to turning pretrained large language models (LLMs) into aligned, capable, and deployable systems. Recent progress spans supervised fine-tuning (SFT…
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation
Hui Wang, Jinghua Zhao, Yifan Yang +9
Generative speech technologies are progressing rapidly, but evaluating the perceptual quality of synthetic speech remains a core challenge. Existing methods typically rely on scala…
Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models
Tai An, Ruwu Cai, Yanzhe Zhang +6
In the era of large language models (LLMs), N:M sparsity has emerged as a structured compression technique critical for accelerating inference. While prior work has primarily focus…