LLMs Are Surprisingly Bad Blog Authors (wakamoleguy.com)

Recent analyses reveal that large language models (LLMs) struggle significantly with producing engaging and informative blog posts, primarily due to their inherent optimization for predictability rather than surprise. It turns out that the predictable, token-based nature of LLMs results in "slop"—content that lacks the surprising elements necessary to captivate readers. The challenge lies in the fundamental mechanics of information theory: more surprising outputs can convey more information, while overly predictable text becomes forgettable. Surprisingly, LLMs often reinforce mundane outcomes, leading to disengagement from the audience. This finding holds considerable significance for the AI/ML community, especially in applications related to content creation. Rather than relying on LLMs for generating original thoughts or captivating narratives, they may serve better as tools for consolidating and structuring verbose content. The key takeaway is that effective writing necessitates a human touch, merging creativity with a dash of unpredictability. As such, writers are encouraged to draft their ideas authentically, leveraging LLMs for research and feedback, rather than expecting them to produce compelling narratives from the outset.