17 citations · 55 across the 13 of their papers we have counts for
13 papers
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
Yuzhao Heng, Chunyuan Deng, Yitong Li +4
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity re…
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li +4
Large Language Models (LLMs) have recently showcased remarkable reasoning abilities. However, larger models often surpass their smaller counterparts in reasoning tasks, posing the…
Local Boosting for Weakly-Supervised Learning
Rongzhi Zhang, Yue Yu, Jiaming Shen +2
Boosting is a commonly used technique to enhance the performance of a set of base models by combining them into a strong ensemble model. Though widely adopted, boosting is typicall…
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
Yue Yu, Yuchen Zhuang, Rongzhi Zhang +3
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data…
Do Not Blindly Imitate the Teacher: Using Perturbed Loss for Knowledge Distillation
Rongzhi Zhang, Jiaming Shen, Tianqi Liu +4
Knowledge distillation is a popular technique to transfer knowledge from large teacher models to a small student model. Typically, the student learns to imitate the teacher by mini…
Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach
Yue Yu, Rongzhi Zhang, Ran Xu +3
Large Language Models have demonstrated remarkable few-shot performance, but the performance can be sensitive to the selection of few-shot instances. We propose PATRON, a new metho…
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
Rongzhi Zhang, Rebecca West, Xiquan Cui +1
On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience…
PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Rongzhi Zhang, Yue Yu, Pranav Shetty +2
Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set…
Attributed Graph Neural Networks for Recommendation Systems on Large-Scale and Sparse Graph
Yifei Zhao, Mingdong Ou, Rongzhi Zhang +1
Link prediction in structured-data is an important problem for many applications, especially for recommendation systems. Existing methods focus on how to learn the node representat…
Knowledge-enhanced Session-based Recommendation with Temporal Transformer
Rongzhi Zhang, Yulong Gu, Xiaoyu Shen +1
Recent research has achieved impressive progress in the session-based recommendation. However, information such as item knowledge and click time interval, which could be potentiall…
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language Models
Yue Yu, Lingkai Kong, Jieyu Zhang +2
While pre-trained language model (PLM) fine-tuning has achieved strong performance in many NLP tasks, the fine-tuning stage can be still demanding in labeled data. Recent works hav…
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Rongzhi Zhang, Yue Yu, Chao Zhang
Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteratio…
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
Hui Su, Xiaoyu Shen, Rongzhi Zhang +4
Recent research has made impressive progress in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challe…