1 code implementation • ACL 2022 • Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang
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 is tedious and difficult.
1 code implementation • NAACL 2022 • Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang
We develop AcTune, a new framework that improves the label efficiency of active PLM fine-tuning by unleashing the power of unlabeled data via self-training.
1 code implementation • 17 Mar 2024 • Yuzhao Heng, Chunyuan Deng, Yitong Li, Yue Yu, Yinghao Li, Rongzhi Zhang, Chao Zhang
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER).
no code implementations • 24 Jan 2024 • Haorui Wang, Rongzhi Zhang, Yinghao Li, Lingkai Kong, Yuchen Zhuang, Xiusi Chen, Chao Zhang
The teacher LLM generates problem-solving instructions and corrective principles based on the student LLM's errors.
no code implementations • 5 Jun 2023 • Rongzhi Zhang, Yue Yu, Jiaming Shen, Xiquan Cui, Chao Zhang
In this work, we show that the standard implementation of the convex combination of base learners can hardly work due to the presence of noisy labels.
1 code implementation • 18 May 2023 • Yue Yu, Yuchen Zhuang, Rongzhi Zhang, Yu Meng, Jiaming Shen, Chao Zhang
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks.
Ranked #1 on Zero-Shot Text Classification on AG News
no code implementations • 8 May 2023 • Rongzhi Zhang, Jiaming Shen, Tianqi Liu, Jialu Liu, Michael Bendersky, Marc Najork, Chao Zhang
In this work, we argue that such a learning objective is sub-optimal because there exists a discrepancy between the teacher's output distribution and the ground truth label distribution.
1 code implementation • 15 Sep 2022 • Yue Yu, Rongzhi Zhang, ran Xu, Jieyu Zhang, Jiaming Shen, Chao Zhang
Large Language Models have demonstrated remarkable few-shot performance, but the performance can be sensitive to the selection of few-shot instances.
no code implementations • 28 Jun 2022 • Rongzhi Zhang, Rebecca West, Xiquan Cui, Chao Zhang
We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions.
1 code implementation • 18 Mar 2022 • Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang
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 is tedious and difficult.
1 code implementation • 16 Dec 2021 • Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang
We propose {\ours}, a new framework that leverages unlabeled data to improve the label efficiency of active PLM fine-tuning.
no code implementations • 16 Dec 2021 • Rongzhi Zhang, Yulong Gu, Xiaoyu Shen, Hui Su
We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer).
1 code implementation • EMNLP 2020 • Rongzhi Zhang, Yue Yu, Chao Zhang
Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration.
1 code implementation • ACL 2019 • Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, Jie zhou
To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.