Imagine teaching an AI to write summaries without needing constant human feedback. That’s the challenge tackled by researchers in “Model-based Preference Optimization in Abstractive Summarization without Human Feedback.” Large Language Models (LLMs) excel at generating fluent text, but they sometimes fabricate information, or 'hallucinate,' details not present in the original. The typical approach to improve accuracy involves training LLMs with human feedback, but this process is expensive and time-consuming. This new research introduces a clever technique called Model-based Preference Optimization (MPO). Instead of relying on humans, MPO leverages the model's existing abilities. It generates multiple summaries using different methods—one producing more accurate but less creative summaries, and another creating more fluent but potentially less accurate ones. By comparing these, the model learns to prefer accuracy without direct human guidance. Tests on standard summarization datasets reveal that MPO significantly improves the quality and truthfulness of summaries without human intervention. This breakthrough has significant implications for automating tasks like news summarization and report generation. However, future research needs to address the current trend of MPO-trained models occasionally copying text directly from the source, a less creative form of summarization. The goal is to strike the right balance between accuracy and generating genuinely insightful summaries that benefit from the power of AI.