Large language models (LLMs) are revolutionizing how we interact with documents, but their ability to extract key information still has room for improvement. Current methods for training LLMs on document understanding tasks often rely on simple, repetitive templates like "What is the value for the {key}?" While convenient, this approach creates a gap between the structured world of datasets and the messy reality of how humans actually ask questions. Researchers at JPMorgan AI Research explore this challenge in their paper "What is the value of {templates}?" They argue that these simplistic templates create datasets that lack the richness and complexity necessary to build truly robust document AI. Imagine trying to learn a language by only ever hearing the same sentence structure repeated over and over. You’d miss the nuances, the exceptions, the real-world ways people express themselves. The team introduces K2Q, a new dataset collection that moves beyond these limitations. Instead of one-size-fits-all templates, K2Q uses a diverse set of over 100 bespoke templates for each document type. This results in a much richer learning experience for the LLMs, exposing them to varied phrasing, questions that involve multiple entities, and even "true/false" type questions. This approach closes the gap between training data and real-world queries, making the models more adaptable and less prone to errors. The results are impressive. When tested on these more complex and realistic questions, LLMs trained on K2Q significantly outperform those trained on simpler datasets. They also demonstrate a better understanding of the document context, generating answers that are "grounded" in the document's actual content, even if the answer isn't perfectly accurate. This groundedness is crucial, as it makes the models' outputs easier to verify and trust. While K2Q represents a significant advance, the researchers acknowledge that challenges remain. Manually crafting high-quality templates is still time-consuming. They suggest that future research could explore using LLMs themselves to generate these templates, further automating the creation of robust datasets. The quest for better document AI is ongoing, but K2Q paves the way for more intelligent and adaptable models that can truly understand the information locked within our documents.