7 citations · 11 across the 6 of their papers we have counts for
6 papers
Can LLMs Take Retrieved Information with a Grain of Salt?
Behzad Shayegh, Mohamed Osama Ahmed, Fred Tung +1
Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to appropriately adapt responses t…
Radar: Fast Long-Context Decoding for Any Transformer
Yongchang Hao, Mengyao Zhai, Hossein Hajimirsadeghi +2
Transformer models have demonstrated exceptional performance across a wide range of applications. Though forming the foundation of Transformer models, the dot-product attention doe…
Were RNNs All We Needed?
Leo Feng, Frederick Tung, Mohamed Osama Ahmed +2
The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers have since achieved widespread success ac…
Attention as an RNN
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi +3
The advent of Transformers marked a significant breakthrough in sequence modelling, providing a highly performant architecture capable of leveraging GPU parallelism. However, Trans…
Prompting-based Temporal Domain Generalization
Sepidehsadat Hosseini, Mengyao Zhai, Hossein Hajimirsadegh +1
Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution…
Tree Cross Attention
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi +2
Cross Attention is a popular method for retrieving information from a set of context tokens for making predictions. At inference time, for each prediction, Cross Attention scans th…