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Found 19 papers, 13 papers with code
Mapping the Increasing Use of LLMs in Scientific Papers
To address this gap, we conduct the first systematic, large-scale analysis across 950, 965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time.
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM).
GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick
Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty.
Perils of Self-Feedback: Self-Bias Amplifies in Large Language Models
Recent studies show that self-feedback improves large language models (LLMs) on certain tasks while worsens other tasks.
DE-COP: Detecting Copyrighted Content in Language Models Training Data
We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text.
Permute-and-Flip: An optimally robust and watermarkable decoder for LLMs
In this paper, we propose a new decoding method called Permute-and-Flip (PF) decoder.
Weak-to-Strong Jailbreaking on Large Language Models
In this paper, we propose the weak-to-strong jailbreaking attack, an efficient method to attack aligned LLMs to produce harmful text.
A Survey on Detection of LLMs-Generated Content
The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education.
Provable Robust Watermarking for AI-Generated Text
We propose a robust and high-quality watermark method, Unigram-Watermark, by extending an existing approach with a simplified fixed grouping strategy.
"Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter
Most existing approaches of differentially private (DP) machine learning focus on private training.
Invisible Image Watermarks Are Provably Removable Using Generative AI
However, if we do not require the watermarked image to look the same as the original one, watermarks that keep the image semantically similar can be an alternative defense against our attack.
Protecting Language Generation Models via Invisible Watermarking
We can then detect the secret message by probing a suspect model to tell if it is distilled from the protected one.
Pre-trained Language Models Can be Fully Zero-Shot Learners
How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data?
Distillation-Resistant Watermarking for Model Protection in NLP
We prove that a protected model still retains the original accuracy within a certain bound.
Provably Confidential Language Modelling
Large language models are shown to memorize privacy information such as social security numbers in training data.
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
How to learn highly compact yet effective sentence representation?
An Optimal Reduction of TV-Denoising to Adaptive Online Learning
We consider the problem of estimating a function from $n$ noisy samples whose discrete Total Variation (TV) is bounded by $C_n$.
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning
In real-world applications, networks usually consist of billions of various types of nodes and edges with abundant attributes.
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET).
