Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models

This research proposes an unsupervised method to identify community-specific slang and unique entities by analyzing the magnitude of semantic shift. Semantic shift is defined as the evolution of a word's encoded representation after fine-tuning a pre-trained Large Language Model (LLM) on a community-specific text corpus.