Comparative Analysis: Fine-Tuning Causal LLMs for Text Classification

This research paper from ArXiv explores the comparative efficacy of embedding-based and instruction-based fine-tuning methods for causal Large Language Models in the context of text classification. The study likely offers valuable insights for practitioners seeking to optimize LLM performance in various text-related tasks.

• •The research investigates different fine-tuning strategies for LLMs.
• •The comparison focuses on embedding-based vs. instruction-based methods.
• •The goal is to enhance text classification performance.