Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models

arXiv:2403.02121v1 Announce Type: new
Abstract: The advent of Large Language Models (LLMs) has advanced the benchmark in various Natural Language Processing (NLP) tasks. However, large amounts of labelled training data are required to train LLMs. Furthermore, data annotation and training are computationally expensive and time-consuming. Zero and few-shot learning have recently emerged as viable options for labelling data using large pre-trained models. Hate speech detection in mix-code low-resource languages is an active problem area where the use of LLMs has …

abstract advanced annotated data annotation arxiv benchmark code cs.ai cs.cl data data annotation detection hate speech hate speech detection language language models language processing large language large language models llms mixed natural natural language natural language processing nlp processing speech tasks train training training data transfer transfer learning type