RuleR: Improving LLM Controllability by Rule-based Data Recycling
<|reference_start|>RuleR: Improving LLM Controllability by Rule-based Data Recycling: Despite the remarkable advancement of Large language models (LLMs), they still lack delicate controllability under sophisticated constraints, which is critical to enhancing their response quality and the user experience. While conditional supervised fine-tuning (SFT) can potentially improve LLM controllability, curating new SFT data to fulfill the constraints usually relies on human experts or proprietary LLMs, which is time-consuming and expensive. To bridge this gap, we propose Rule-based Data Recycling (RuleR), a human/LLM-free data augmentation method incorporating multiple constraints into the original SFT data. Instead of creating new responses from scratch, RuleR integrates linguistic or formatting rules into the original instructions and modifies the responses to fulfill the rule-defined constraints. Training on the "recycled" data consolidates LLMs capability to generate constrained outputs. Extensive experiments demonstrate RuleR's effectiveness in improving LLM controllability while maintaining general instruction-following performance. RuleR's code is released on https://github.com/tianyi-lab/RuleR.<|reference_end|>
@article{li2024ruler:,
    title={RuleR: Improving LLM Controllability by Rule-based Data Recycling},
    author={Ming Li, Han Chen, Chenguang Wang, Dang Nguyen, Dianqi Li, Tianyi Zhou},
    journal={arXiv preprint arXiv:2406.15938},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15938},
    primaryClass={cs.CL cs.AI cs.LG}
}