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Boosted Prompt Ensembles for Large Language Models

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arxiv 2304.05970 v1 pith:NCRT4WY7 submitted 2023-04-12 cs.CL cs.LG

Boosted Prompt Ensembles for Large Language Models

Silviu Pitis , Michael R. Zhang , Andrew Wang , Jimmy Ba This is my paper
classification cs.CL cs.LG
keywords promptboostedensembleslanguageensembleexampleslargemodels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training. To further improve performance, we propose a prompt ensembling method for large language models, which uses a small dataset to construct a set of few shot prompts that together comprise a ``boosted prompt ensemble''. The few shot examples for each prompt are chosen in a stepwise fashion to be ``hard'' examples on which the previous step's ensemble is uncertain. We show that this outperforms single-prompt output-space ensembles and bagged prompt-space ensembles on the GSM8k and AQuA datasets, among others. We propose both train-time and test-time versions of boosted prompting that use different levels of available annotation and conduct a detailed empirical study of our algorithm.

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