Large language models (LLMs) like ChatGPT have become incredibly powerful tools for generating text, translating languages, and even writing different kinds of creative content. But how good are they at judging the quality of information, especially when that information comes from different sources, including themselves? New research explores this question, focusing on how LLMs perform in retrieval-augmented generation (RAG) systems. RAG combines information retrieval with LLMs to generate more accurate and relevant content. This study reveals some surprising findings about how LLMs evaluate information in these systems. The research simulated two key phases of a RAG system. First, it looked at how LLMs rank the relevance of different passages for answering specific questions. Second, it examined how LLMs choose between different passages when generating answers. One might expect that LLMs would show a bias toward their own generated text, a kind of 'self-preference.' Surprisingly, the research found that in RAG settings, this bias is minimal. Instead, LLMs prioritize factual accuracy, even when the correct information comes from a human-written source rather than their own output. However, this fairness isn’t absolute. The research discovered that while LLMs are generally good at identifying factual information, they can be influenced by writing style. For instance, if a passage is written in a way that closely mirrors the original question, LLMs tend to favor it, even if other passages contain equally valid information. This suggests that while LLMs are becoming more sophisticated in how they process information, they can still be swayed by stylistic factors. The implications of these findings are significant for the future development of RAG systems. It suggests that we can rely on LLMs to be relatively unbiased judges of information within these systems, which is crucial for generating accurate and trustworthy content. However, it also highlights the need for further research into how stylistic features can influence LLM decision-making. By understanding these subtle biases, we can develop strategies to mitigate their impact and build even more robust and reliable AI systems.