195 citations · 350 across the 23 of their papers we have counts for
9 papers · 1 filter
Sparse Universal Transformer
Shawn Tan, Yikang Shen, Zhenfang Chen +2
The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers. Empirical evidence shows that UTs have better compositional generalization…
SALMON: Self-Alignment with Instructable Reward Models
Zhiqing Sun, Yikang Shen, Hongxin Zhang +5
Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI ag…
ModuleFormer: Modularity Emerges from Mixture-of-Experts
Yikang Shen, Zheyu Zhang, Tianyou Cao +3
Large Language Models (LLMs) have achieved remarkable results. However, existing models are expensive to train and deploy, and it is also difficult to expand their knowledge beyond…
Transformer-Patcher: One Mistake worth One Neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang +3
Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For…
Mixture of Attention Heads: Selecting Attention Heads Per Token
Xiaofeng Zhang, Yikang Shen, Zeyu Huang +3
Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. However, the study of MoE components mostly…
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
Yikang Shen, Yi Tay, Che Zheng +3
There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the…
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle
Yikang Shen, Shawn Tan, Alessandro Sordoni +2
Syntax is fundamental to our thinking about language. Failing to capture the structure of input language could lead to generalization problems and over-parametrization. In the pres…
Recursive Top-Down Production for Sentence Generation with Latent Trees
Shawn Tan, Yikang Shen, Timothy J. O'Donnell +2
We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic programming algorithm that marginalises o…
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Wenyu Du, Zhouhan Lin, Yikang Shen +3
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structu…