Study Explains Why Large Language Models Struggle With Vast Data Inputs

A new study identifies a phenomenon called Context-Entropy Collapse that explains why enterprise Large Language Models (LLMs) fail when fed massive amounts of data. The research shows that as the volume of input data grows, the model's attention mechanism dilutes, causing it to ignore critical instructions. This leads to errors such as factual confabulation and increased processing delays. The findings have been formally documented and published in an academic manuscript.