no code implementations • 22 Apr 2024 • Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohammed Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Chenxi Whitehouse, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30).
no code implementations • 17 Feb 2024 • Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohanned Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels.
1 code implementation • 2 Feb 2024 • Jinyan Su, Peilin Yu, Jieyu Zhang, Stephen H. Bach
We propose a Structure Refining Module, a simple yet effective first approach based on the similarities of the prompts by taking advantage of the intrinsic structure in the embedding space.
1 code implementation • 2 Nov 2023 • Jinyan Su, Claire Cardie, Preslav Nakov
With the proliferation of both human-written and machine-generated real and fake news, robustly and effectively discerning the veracity of news articles has become an intricate challenge.
no code implementations • 15 Sep 2023 • Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, Preslav Nakov
The spread of fake news has emerged as a critical challenge, undermining trust and posing threats to society.
2 code implementations • 24 May 2023 • Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
These results show that the problem is far from solved and that there is a lot of room for improvement.
1 code implementation • 23 May 2023 • Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov
One is called DetectLLM-LRR, which is fast and efficient, and the other is called DetectLLM-NPR, which is more accurate, but slower due to the need for perturbations.
no code implementations • 31 Mar 2023 • Jinyan Su, Changhong Zhao, Di Wang
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces.
no code implementations • 17 Sep 2022 • Jinyan Su, Jinhui Xu, Di Wang
In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP).
no code implementations • 31 Jul 2021 • Jinyan Su, Lijie Hu, Di Wang
Specifically, we first show that under some mild assumptions on the loss functions, there is an algorithm whose output could achieve an upper bound of $\tilde{O}((\frac{1}{\sqrt{n}}+\frac{\sqrt{d\log \frac{1}{\delta}}}{n\epsilon})^\frac{\theta}{\theta-1})$ for $(\epsilon, \delta)$-DP when $\theta\geq 2$, here $n$ is the sample size and $d$ is the dimension of the space.