Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Large language models (LLM) have demonstrated emergent abilities in text generation,
question answering, and reasoning, facilitating various tasks and domains. Despite their …

Large Language Models (LLMs) are distinguished by their massive parameter counts, which
typically result in significant redundancy. This work introduces MaskLLM, a learnable …

Despite their nearly universal adoption for large language models, the internal workings of
transformers are not well understood. We aim to better understand the impact of removing or …

Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by
demonstrating exceptional performance across various tasks. However, substantial …

Recent work on pruning large language models (LLMs) has shown that one can eliminate a
large number of parameters without compromising performance, making pruning a …

Large Language Models (LLMs) are not only resource-intensive to train but even more
costly to deploy in production. Therefore, recent work has attempted to prune blocks of LLMs …

Sequential Recommendation (SR) task involves predicting the next item a user is likely to
interact with, given their past interactions. The SR models examine the sequence of a user's …