extraction template, we add the “None” answer if the key does not exist in the given document. To increase diversity in
the SFT training data, we also derive internal classification and MCQ instructions from original KIE annotations. To stay
consistent with benchmarks from previous work [31, 32], we only keep the prompts derived from the extraction template
in the test split of each KIE dataset. An example extraction instruction derived from KLC would read: "{document}
What is the value for the \"charity number\"?"
Document Classification. We aggregate RVL-CDIP [55] and BizDocs to build our CLS instruction-tuning data. We
used two types of instruction templates for this task: internal classification and MCQ, as shown in 1. To avoid the cold
start problem induced by potentially unseen types of documents in testing or even in production usage, we only keep
the MCQ prompts for the test split of each CLS dataset. We also downsample RVL-CDIP in the train split to avoid
DOCLLM