The field of natural language processing (NLP) is witnessing significant advancements with the integration of large language models (LLMs) in various tasks. One of the key trends is the application of LLMs in detecting hate speech, fraud, and fake news across multiple languages. Researchers are exploring the potential of LLMs in zero-shot and few-shot learning, which enables them to generalize well to unseen data and adapt to new languages with minimal training. The use of LLMs is also being extended to low-resource languages, such as Urdu and Arabic, where they are being used for intent detection, sentiment analysis, and other NLP tasks. Furthermore, LLMs are being used in stance detection, which is essential for understanding subjective content across various platforms. Noteworthy papers in this area include the study on prompting LLMs for hate speech detection across languages, which highlights the importance of prompt design in maximizing performance. Another notable work is the introduction of a multi-agent system with LLMs for fake news detection, which enhances the interpretability and effectiveness of detection methods.
Advancements in Large Language Models for NLP Tasks
Sources
Can Prompting LLMs Unlock Hate Speech Detection across Languages? A Zero-shot and Few-shot Study
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines
Large Language Models and Arabic Content: A Review
Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions
The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News
Comparing LLM Text Annotation Skills: A Study on Human Rights Violations in Social Media Data