  Controlling the format of outputs generated by large language models (LLMs)
is a critical functionality in various applications. Current methods typically
employ constrained decoding with rule-based automata or fine-tuning with
manually crafted format instructions, both of which struggle with open-domain
format requirements. To address this limitation, we introduce a novel framework
for controlled generation in LLMs, leveraging user-provided, one-shot QA pairs.
This study investigates LLMs' capabilities to follow open-domain, one-shot
constraints and replicate the format of the example answers. We observe that
this is a non-trivial problem for current LLMs. We also develop a dataset
collection methodology for supervised fine-tuning that enhances the open-domain
format control of LLMs without degrading output quality, as well as a benchmark
on which we evaluate both the helpfulness and format correctness of LLM
outputs. The resulting datasets, named OIFC-SFT, along with the related code,
will be made publicly available at https://github.com/cofe-ai/OIFC.
