most citedEpitaxial growth and electronic structure of Ruddlesden-Popper nickelates ()

74 citations · 82 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20231 cited

Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals

Zhiyuan Hu, Yue Feng, Yang Deng +4

Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular…

cs.CV20231 cited

IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization

Zekun Li, Lei Qi, Yinghuan Shi +1

Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled…

cs.CL20232 cited

Evaluating the Instruction-Following Robustness of Large Language Models to Prompt Injection

Zekun Li, Baolin Peng, Pengcheng He +1

Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, becoming increasingly crucial across various applications. However, this capability…

cond-mat.supr-con202374 cited

Epitaxial growth and electronic structure of Ruddlesden-Popper nickelates ()

Zi Li, Wei Guo, Tingting Zhang +4

We report the epitaxial growth of Ruddlesden-Popper nickelates, , with up to 5 by reactive molecular beam epitaxy (MBE).…

cs.CL20224 cited

Limitations of Language Models in Arithmetic and Symbolic Induction

Jing Qian, Hong Wang, Zekun Li +2

Recent work has shown that large pretrained Language Models (LMs) can not only perform remarkably well on a range of Natural Language Processing (NLP) tasks but also start improvin…