5 citations · 27 across the 26 of their papers we have counts for
8 papers · 1 filter
Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers
Shwai He, Tao Ge, Guoheng Sun +3
Traditional transformer models often allocate a fixed amount of computational resources to every input token, leading to inefficient and unnecessary computation. To address this, t…
What Matters in Transformers? Not All Attention is Needed
Shwai He, Guoheng Sun, Zheyu Shen +1
While scaling Transformer-based large language models (LLMs) has demonstrated promising performance across various tasks, it also introduces redundant architectures, posing efficie…
Loki: Low-rank Keys for Efficient Sparse Attention
Prajwal Singhania, Siddharth Singh, Shwai He +2
Inference on large language models (LLMs) can be expensive in terms of the compute and memory costs involved, especially when long sequence lengths are used. In particular, the sel…
Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques
Shwai He, Daize Dong, Liang Ding +1
Scaling large language models has driven remarkable advancements across various domains, yet the continual increase in model size presents significant challenges for real-world dep…
Rethinking Pruning for Vision-Language Models: Strategies for Effective Sparsity and Performance Restoration
Shwai He, Ang Li, Tianlong Chen
Vision-Language Models (VLMs) integrate information from multiple modalities and have shown remarkable success across various tasks. However, deploying large-scale VLMs in resource…
Reformatted Alignment
Run-Ze Fan, Xuefeng Li, Haoyang Zou +5
The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone t…
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +3
Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the tra…
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Ming Li, Yong Zhang, Shwai He +5
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both…