In the world of Large Language Models (LLMs), bigger isn't always better, especially when it comes to context length. Simply feeding an LLM more text doesn't guarantee it can effectively use that information. A new research paper, "Long Context is Not Long at All," reveals why current LLMs struggle with long contexts and introduces a solution: ProLong. The core problem is that many long texts lack genuine long-range dependencies. Think of a lengthy document made by randomly stringing together unrelated sentences—long in form, but short in meaningful connections. ProLong tackles this by scoring training data based on the strength and specificity of dependencies between text segments. This allows researchers to cherry-pick data with rich, interconnected information, leading to more effective long-context learning. The results are impressive. LLMs trained with ProLong's filtered data outperform those trained on much larger datasets of randomly concatenated text. This suggests a paradigm shift in how we approach long-context training for LLMs. Instead of blindly increasing context windows, we need to focus on the quality and interconnectedness of the data itself. ProLong offers a promising path toward unlocking the true potential of long-context LLMs, paving the way for more sophisticated AI applications that can handle complex, information-rich tasks.