Learning from Self Critique and Refinement for Faithful LLM Summarization

This article, sourced from ArXiv, focuses on improving the faithfulness of Large Language Model (LLM) summarization. It likely explores methods where the LLM critiques its own summaries and refines them based on this self-assessment. The research aims to address the common issue of LLMs generating inaccurate or misleading summaries.