7 citations · 10 across the 5 of their papers we have counts for
5 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…
Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?
Mir Rayat Imtiaz Hossain, Leo Feng, Leonid Sigal +1
Large Language Models (LLMs) have emerged as promising recommendation systems, offering novel ways to model user preferences through generative approaches. However, many existing m…
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…
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…