Open-domain Implicit Format Control for Large Language Model Generation
<|reference_start|>Open-domain Implicit Format Control for Large Language Model Generation: 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.<|reference_end|>
@article{yao2024open-domain,
    title={Open-domain Implicit Format Control for Large Language Model Generation},
    author={Yiqun Yao, Wenjia Ma, Xuezhi Fang, Xin Jiang, Xiang Li, Xuying Meng,
  Peng Han, Jing Li, Aixin Sun, Yequan Wang},
    journal={arXiv preprint arXiv:2408.04392},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04392},
    primaryClass={cs.CL}
}