  We study the problem of automatically annotating relevant numerals (GAAP
metrics) occurring in the financial documents with their corresponding XBRL
tags. Different from prior works, we investigate the feasibility of solving
this extreme classification problem using a generative paradigm through
instruction tuning of Large Language Models (LLMs). To this end, we leverage
metric metadata information to frame our target outputs while proposing a
parameter efficient solution for the task using LoRA. We perform experiments on
two recently released financial numeric labeling datasets. Our proposed model,
FLAN-FinXC, achieves new state-of-the-art performances on both the datasets,
outperforming several strong baselines. We explain the better scores of our
proposed model by demonstrating its capability for zero-shot as well as the
least frequently occurring tags. Also, even when we fail to predict the XBRL
tags correctly, our generated output has substantial overlap with the
ground-truth in majority of the cases.
