Liyuan Liu

Learning Named Entity Tagger using Domain-Specific Dictionary

2018 · arXiv (Cornell University)

Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing …

LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy

2024 · arXiv (Cornell University)

The Key-Value (KV) cache is a crucial component in serving transformer-based autoregressive large language models (LLMs), enabling faster inference by storing previously computed KV vectors. However, its memory consumption scales linearly with sequence length and …

Empower Sequence Labeling with Task-Aware Neural Language Model

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Linguistic sequence labeling is a general approach encompassing a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted …

Empower Sequence Labeling with Task-Aware Neural Language Model

2017 · arXiv (Cornell University)

Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models …