1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 1 cited
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
Zhu Xu, Zhiqiang Zhao, Zihan Zhang +6
Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. T…
cs.LG2024
Combining Relevance and Magnitude for Resource-Aware DNN Pruning
Carla Fabiana Chiasserini, Francesco Malandrino, Nuria Molner +1
Pruning neural networks, i.e., removing some of their parameters whilst retaining their accuracy, is one of the main ways to reduce the latency of a machine learning pipeline, espe…