Can large language models (LLMs) be fair judges? New research suggests they might struggle with impartiality, especially when evaluating their own work. This 'self-preference bias' raises concerns about using LLMs to assess the quality of other AI-generated text, potentially stifling innovation and reinforcing existing biases. Researchers have developed a new metric to quantify this bias, revealing that some LLMs, particularly GPT-4, show a strong tendency to favor their own responses, even when human evaluators disagree. The study delves into the potential reasons behind this bias, exploring the link between an LLM's familiarity with text (measured by perplexity) and its assigned score. The findings indicate that LLMs tend to give higher marks to text they find more predictable, regardless of whether they generated it themselves. This suggests that self-preference isn't just about ego, but about how LLMs perceive and process information. While this bias poses a challenge for using LLMs as reliable evaluators, the research also points towards potential solutions, like ensemble evaluation using multiple models to balance individual biases and refine the judgment process. This research not only highlights a crucial limitation of current LLMs but also paves the way for developing more objective and trustworthy AI evaluation systems.