4 papers · 1 filter
ClaHF: A Human Feedback-inspired Reinforcement Learning Framework for Improving Classification Tasks
Tianxiang Xu, Xiaoyan Zhu, Xin Lai +1
Text classification models are typically trained via supervised fine-tuning (SFT). However, SFT essentially performs behavior cloning from instance-wise labels and thus fails to ad…
Do Papers Tell the Whole Story? A Benchmark and Framework for Uncovering Hidden Implementation Gaps in Bioinformatics
Tianxiang Xu, Xiaoyan Zhu, Xin Lai +4
As bioinformatics software is increasingly applied across a broader range of scenarios and the rapid development of large language models (LLMs) further lowers the barriers to soft…
Making Every Head Count: Sparse Attention Without the Speed-Performance Trade-off
Mingkuan Zhao, Wentao Hu, Jiayin Wang +5
The design of Large Language Models (LLMs) has long been hampered by a fundamental conflict within their core attention mechanism: its remarkable expressivity is built upon a compu…
Mosaic Pruning: A Hierarchical Framework for Generalizable Pruning of Mixture-of-Experts Models
Wentao Hu, Mingkuan Zhao, Shuangyong Song +3
Sparse Mixture-of-Experts (SMoE) architectures have enabled a new frontier in scaling Large Language Models (LLMs), offering superior performance by activating only a fraction of t…