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Found 16 papers, 6 papers with code
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges
We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.
Text Transformations in Contrastive Self-Supervised Learning: A Review
Contrastive self-supervised learning has become a prominent technique in representation learning.
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach
In this paper we address the solution of the popular Wordle puzzle, using new reinforcement learning methods, which apply more generally to adaptive control of dynamic systems and to classes of Partially Observable Markov Decision Process (POMDP) problems.
Towards Detecting Harmful Agendas in News Articles
Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.
Stylometric Detection of AI-Generated Text in Twitter Timelines
However, tweets are inherently short, thus making it difficult for current state-of-the-art pre-trained language model-based detectors to accurately detect at what point the AI starts to generate tweets in a given Twitter timeline.
Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?
Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale.
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News
The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape.
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text.
Towards LLM-guided Causal Explainability for Black-box Text Classifiers
Inspired by recent endeavors to utilize Large Language Models (LLMs) as experts, in this work, we aim to leverage the instruction-following and textual understanding capabilities of recent state-of-the-art LLMs to facilitate causal explainability via counterfactual explanation generation for black-box text classifiers.
Adversarial Text Purification: A Large Language Model Approach for Defense
Adversarial purification is a defense mechanism for safeguarding classifiers against adversarial attacks without knowing the type of attacks or training of the classifier.
ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement
While it is highly recommended that applicants tailor their resume to the specific role they are applying for, manually tailoring resumes to job descriptions and role-specific requirements is often (1) extremely time-consuming, and (2) prone to human errors.
Large Language Models for Data Annotation: A Survey
Furthermore, the paper includes an in-depth taxonomy of methodologies employing LLMs for data annotation, a comprehensive review of learning strategies for models incorporating LLM-generated annotations, and a detailed discussion on primary challenges and limitations associated with using LLMs for data annotation.
Harnessing Artificial Intelligence to Combat Online Hate: Exploring the Challenges and Opportunities of Large Language Models in Hate Speech Detection
Large language models (LLMs) excel in many diverse applications beyond language generation, e. g., translation, summarization, and sentiment analysis.
Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales
Although social media platforms are a prominent arena for users to engage in interpersonal discussions and express opinions, the facade and anonymity offered by social media may allow users to spew hate speech and offensive content.
EAGLE: A Domain Generalization Framework for AI-generated Text Detection
With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models.
