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Found 9 papers, 4 papers with code
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels.
Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision
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.
Adapting Fake News Detection to the Era of Large Language Models
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.
Fake News Detectors are Biased against Texts Generated by Large Language Models
The spread of fake news has emerged as a critical challenge, undermining trust and posing threats to society.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
These results show that the problem is far from solved and that there is a lot of room for improvement.
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text
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.
Differentially Private Stochastic Convex Optimization in (Non)-Euclidean Space Revisited
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general $\ell_p^d$ spaces.
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data
In this paper, we study the problem of PAC learning halfspaces in the non-interactive local differential privacy model (NLDP).
Faster Rates of Private Stochastic Convex Optimization
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.
