Search Results for author: Thai Le

Found 25 papers, 10 papers with code

Authorship Attribution for Neural Text Generation

no code implementations EMNLP 2020 Adaku Uchendu, Thai Le, Kai Shu, Dongwon Lee

In recent years, the task of generating realistic short and long texts have made tremendous advancements.

Text Generation

Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning

no code implementations16 Feb 2024 Tuc Nguyen, Thai Le

Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of them at once.

Computational Efficiency

ALISON: Fast and Effective Stylometric Authorship Obfuscation

1 code implementation1 Feb 2024 Eric Xing, Saranya Venkatraman, Thai Le, Dongwon Lee

AO is the corresponding adversarial task, aiming to modify a text in such a way that its semantics are preserved, yet an AA model cannot correctly infer its authorship.

Marrying Adapters and Mixup to Efficiently Enhance the Adversarial Robustness of Pre-Trained Language Models for Text Classification

no code implementations18 Jan 2024 Tuc Nguyen, Thai Le

Existing works show that augmenting training data of neural networks using both clean and adversarial examples can enhance their generalizability under adversarial attacks.

Adversarial Robustness text-classification +1

A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts

no code implementations14 Nov 2023 Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee

In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.

HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis

no code implementations25 Oct 2023 Nafis Irtiza Tripto, Adaku Uchendu, Thai Le, Mattia Setzu, Fosca Giannotti, Dongwon Lee

Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).

Text Detection

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

1 code implementation20 Oct 2023 Dominik Macko, Robert Moro, Adaku Uchendu, Jason Samuel Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova

There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings.

Benchmarking Text Detection

TopRoBERTa: Topology-Aware Authorship Attribution of Deepfake Texts

no code implementations22 Sep 2023 Adaku Uchendu, Thai Le, Dongwon Lee

We propose \textbf{TopRoBERTa} to improve existing AA solutions by capturing more linguistic patterns in deepfake texts by including a Topological Data Analysis (TDA) layer in the RoBERTa model.

Face Swapping Misinformation +2

Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack

1 code implementation21 May 2023 Christopher Burger, Lingwei Chen, Thai Le

LIME has emerged as one of the most commonly referenced tools in explainable AI (XAI) frameworks that is integrated into critical machine learning applications--e. g., healthcare and finance.

Adversarial Attack

Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?

2 code implementations3 Apr 2023 Adaku Uchendu, Jooyoung Lee, Hua Shen, Thai Le, Ting-Hao 'Kenneth' Huang, Dongwon Lee

Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.

Face Swapping Human Detection +1

NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online

no code implementations18 Mar 2023 Yiran Ye, Thai Le, Dongwon Lee

In this paper, we introduce a benchmark test set containing human-written perturbations online for toxic speech detection models.

Adversarial Attack Benchmarking +2

CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild

no code implementations16 Jan 2023 Thai Le, Ye Yiran, Yifan Hu, Dongwon Lee

CRYPTEXT is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations.

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

no code implementations19 Oct 2022 Adaku Uchendu, Thai Le, Dongwon Lee

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO).

Attribute Text Generation

Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense

1 code implementation Findings (ACL) 2022 Thai Le, Jooyoung Lee, Kevin Yen, Yifan Hu, Dongwon Lee

We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.

Adversarial Attack

Do Language Models Plagiarize?

1 code implementation15 Mar 2022 Jooyoung Lee, Thai Le, Jinghui Chen, Dongwon Lee

Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs' plagiarism patterns vary based on their corpus similarity and homogeneity.

Language Modelling Memorization +1

Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning

no code implementations20 Oct 2021 Thai Le, Long Tran-Thanh, Dongwon Lee

To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected.

Adversarial Attack Hierarchical Reinforcement Learning +2

Large-Scale Data-Driven Airline Market Influence Maximization

no code implementations31 May 2021 Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, Noseong Park

On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.

SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher

1 code implementation ACL 2022 Thai Le, Noseong Park, Dongwon Lee

Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch.

Adversarial Robustness

MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

1 code implementation1 Sep 2020 Thai Le, Suhang Wang, Dongwon Lee

In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem.

Comment Generation Fake News Detection

GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction

1 code implementation5 Nov 2019 Thai Le, Suhang Wang, Dongwon Lee

Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats.


Machine Learning Based Detection of Clickbait Posts in Social Media

no code implementations5 Oct 2017 Xinyue Cao, Thai Le, Jason, Zhang

In this paper, we make use of a dataset from the clickbait challenge 2017 (clickbait-challenge. com) comprising of over 21, 000 headlines/titles, each of which is annotated by at least five judgments from crowdsourcing on how clickbait it is.

BIG-bench Machine Learning Clickbait Detection

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