1 code implementation • 20 Jan 2024 • Pengyu Wang, Dong Zhang, Linyang Li, Chenkun Tan, Xinghao Wang, Ke Ren, Botian Jiang, Xipeng Qiu
With the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications.
1 code implementation • 17 Oct 2023 • Linyang Li, Botian Jiang, Pengyu Wang, Ke Ren, Hang Yan, Xipeng Qiu
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed.
1 code implementation • 13 Oct 2023 • Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang, Dong Zhang, Xipeng Qiu
Therefore, it is important to build strong AI-generated text (AIGT) detectors.
1 code implementation • 13 Oct 2023 • Linyang Li, Ke Ren, Yunfan Shao, Pengyu Wang, Xipeng Qiu
Through experimental results, we find that we can build a connection between discrete and continuous perturbations and use the proposed PerturbScore to learn such correlation, surpassing previous methods used in discrete perturbation measuring.
no code implementations • 27 Apr 2023 • Linyang Li, Pengyu Wang, Ke Ren, Tianxiang Sun, Xipeng Qiu
The extraordinary performance of large language models (LLMs) heightens the importance of detecting whether the context is generated by an AI system.
no code implementations • 7 Jul 2020 • Hoda Bidkhori, John P. Dickerson, Duncan C. McElfresh, Ke Ren
To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical.
no code implementations • 20 Jul 2019 • Ke Ren, Avinash Malik
Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans.
no code implementations • 18 Mar 2018 • Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples.