5 citations · 14 across the 12 of their papers we have counts for
6 papers · 1 filter
Erase-then-Delta Attention: Decoupling Erase and Write Addresses in Delta-Rule Linear Attention
Xiao Li, Chengruidong Zhang, Hao Luo +15
Delta-rule linear attention improves recurrent memory updates by correcting what is already stored at the current write address before writing new content. However, the active corr…
A Controllable Examination for Long-Context Language Models
Yijun Yang, Zeyu Huang, Wenhao Zhu +4
Existing frameworks for evaluating long-context language models (LCLM) can be broadly categorized into real-world applications (e.g, document summarization) and synthetic tasks (e.…
Layerwise Recurrent Router for Mixture-of-Experts
Zihan Qiu, Zeyu Huang, Shuang Cheng +4
The scaling of large language models (LLMs) has revolutionized their capabilities in various tasks, yet this growth must be matched with efficient computational strategies. The Mix…
A Closer Look into Mixture-of-Experts in Large Language Models
Ka Man Lo, Zeyu Huang, Zihan Qiu +2
Mixture-of-experts (MoE) is gaining increasing attention due to its unique properties and remarkable performance, especially for language tasks. By sparsely activating a subset of…
Stacking Your Transformers: A Closer Look at Model Growth for Efficient LLM Pre-Training
Wenyu Du, Tongxu Luo, Zihan Qiu +5
LLMs are computationally expensive to pre-train due to their large scale. Model growth emerges as a promising approach by leveraging smaller models to accelerate the training of la…
Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
Zihan Qiu, Zeyu Huang, Youcheng Huang +1
The feed-forward networks (FFNs) in transformers are recognized as a group of key-value neural memories to restore abstract high-level knowledge. In this work, we conduct an empiri…