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Found 11 papers, 7 papers with code
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.
A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization
We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs) capable of generating high-quality text.
Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey
The contemporary LLMs are prone to producing hallucinations, stemming mainly from the knowledge gaps within the models.
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts
The novel universal evasive prompt is achieved in two steps: First, we create an evasive soft prompt tailored to a specific PLM through prompt tuning; and then, we leverage the transferability of soft prompts to transfer the learned evasive soft prompt from one PLM to another.
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.
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.
Neural Authorship Attribution: Stylometric Analysis on Large Language Models
Large language models (LLMs) such as GPT-4, PaLM, and Llama have significantly propelled the generation of AI-crafted text.
Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts.
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics.
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.
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.
