Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
Continual LLM Upcycling: A Predictor-Gated Bank-Wise Sparsity Training Recipe for Dense-to-Sparse LLMs
Ruixuan Huang, Jinyuan Shi, Hantao Huang +5
We study dense-to-sparse continual training as a way to construct channel-sparse large language models from dense checkpoints. Starting from a Qwen2.5-8B dense backbone, we continu…
cs.CL2026
ConceptRM: The Quest to Mitigate Alert Fatigue through Consensus-Based Purity-Driven Data Cleaning for Reflection Modelling
Yongda Yu, Lei Zhang, Xinxin Guo +9
In many applications involving intelligent agents, the overwhelming volume of alerts (mostly false) generated by the agents may desensitize users and cause them to overlook critica…
cs.CL2025
SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs
Ruixuan Huang, Hao Zeng, Hantao Huang +4
Post-training quantization (PTQ) plays a crucial role in the democratization of large language models (LLMs). However, existing low-bit quantization and sparsification techniques a…