Yue Yu

BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision

2020

We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly incomplete and noisy distant labels via external knowledge bases. …

Bridging pre-trained models and downstream tasks for source code understanding

2022 · Proceedings of the 44th International Conference on Software Engineering

With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively adapt …

Neighborhood-Regularized Self-Training for Learning with Few Labels

2023 · arXiv (Cornell University)

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of …

A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

2023 · arXiv (Cornell University)

This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. We thoroughly analyzed existing …

COPR: Continual Human Preference Learning via Optimal Policy Regularization

2024 · arXiv (Cornell University)

Reinforcement Learning from Human Feedback (RLHF) is commonly utilized to improve the alignment of Large Language Models (LLMs) with human preferences. Given the evolving nature of human preferences, continual alignment becomes more crucial and practical …

DeepServe: Serverless Large Language Model Serving at Scale

2025 · arXiv (Cornell University)

In this paper, we propose DEEPSERVE, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DEEPSERVE addresses key challenges such as resource allocation, serving efficiency, …

PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation

2021 · arXiv (Cornell University)

Large-scale Pretrained Language Models (PLMs) have become the new paradigm for Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as GPT-3 have demonstrated strong performances on natural language understanding and generation with …