Improving Minimum Bayes Risk Decoding with Multi-Prompt
<|reference_start|>Improving Minimum Bayes Risk Decoding with Multi-Prompt: While instruction fine-tuned LLMs are effective text generators, sensitivity to prompt construction makes performance unstable and sub-optimal in practice. Relying on a single "best" prompt cannot capture all differing approaches to a generation problem. Using this observation, we propose multi-prompt decoding, where many candidate generations are decoded from a prompt bank at inference-time. To ensemble candidates, we use Minimum Bayes Risk (MBR) decoding, which selects a final output using a trained value metric. We show multi-prompt improves MBR across a comprehensive set of conditional generation tasks, and show this is a result of estimating a more diverse and higher quality candidate space than that of a single prompt. Further experiments confirm multi-prompt improves generation across tasks, models and metrics.<|reference_end|>
@article{heineman2024improving,
    title={Improving Minimum Bayes Risk Decoding with Multi-Prompt},
    author={David Heineman, Yao Dou, Wei Xu},
    journal={arXiv preprint arXiv:2407.15343},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15343},
    primaryClass={cs.CL}
}