3 papers
cs.CL2026
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…
cs.LG2026
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…
cs.LG2024
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…