Boosted Prompt Ensembles for Large Language Models -Summary

Boosted prompting is a method proposed for large language models that constructs a set of few-shot prompts from a small dataset to form a boosted prompt ensemble, aiming to improve performance on challenging datasets such as GSM8k and AQuA. This approach involves selecting 'hard' examples stepwise to address uncertainty in the ensemble's prior steps and outperforms single-prompt and bagged prompt-space ensembles. It requires minimal manual prompt engineering and can work with as few as 100 labeled examples, complementing other reasoning techniques like chain-of-thought. While it achieves strong results on multiple reasoning benchmarks, its effectiveness is dependent on the quality of the initial prompt, requires sufficient model accuracy for reliable self-supervision, and may not benefit weaker models like Curie. Additionally, it is computationally more expensive than single prompts and generally performs better during train-time than test-time.

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