3 citations · 4 across the 5 of their papers we have counts for
5 papers
Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding
Zheng Zhao, Emilio Monti, Jens Lehmann +1
Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially re…
Aligned Weight Regularizers for Pruning Pretrained Neural Networks
James O' Neill, Sourav Dutta, Haytham Assem
While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on…
Deep Neural Compression Via Concurrent Pruning and Self-Distillation
James O' Neill, Sourav Dutta, Haytham Assem
Pruning aims to reduce the number of parameters while maintaining performance close to the original network. This work proposes a novel \emph{self-distillation} based pruning strat…
Sequence-to-Sequence Learning on Keywords for Efficient FAQ Retrieval
Sourav Dutta, Haytham Assem, Edward Burgin
Frequently-Asked-Question (FAQ) retrieval provides an effective procedure for responding to user's natural language based queries. Such platforms are becoming common in enterprise…
Unsupervised Word Translation Pairing using Refinement based Point Set Registration
Silviu Oprea, Sourav Dutta, Haytham Assem
Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Cu…