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Few-shot question answering (QA) aims at achieving satisfactory results onmachine question answering when only a few training samples are available.Recent advances mostly rely on the power of pre-trained large language models(LLMs) and fine-tuning in specific settings. Although the pre-training stagehas already equipped LLMs with powerful reasoning capabilities, LLMs still needto be fine-tuned to adapt to specific domains to achieve the best results. Inthis paper, we propose to select the most informative data for fine-tuning,thereby improving the efficiency of the fine-tuning process with comparative oreven better accuracy on the open-domain QA task. We present MinPrompt, aminimal data augmentation framework for open-domain QA based on an approximategraph algorithm and unsupervised question generation. We transform the raw textinto a graph structure to build connections between different factualsentences, then apply graph algorithms to identify the minimal set of sentencesneeded to cover the most information in the raw text. We then generate QA pairsbased on the identified sentence subset and train the model on the selectedsentences to obtain the final model. Empirical results on several benchmarkdatasets and theoretical analysis show that MinPrompt is able to achievecomparable or better results than baselines with a high degree of efficiency,bringing improvements in F-1 scores by up to 27.5%.

MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering