MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot
  Question Answering
Xiusi Chen Jyun-Yu Jiang Wei-Cheng Chang Cho-Jui Hsieh Hsiang-Fu Yu Wei Wang
Few-shot question answering (QA) aims at achieving satisfactory results on
machine 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 stage
has already equipped LLMs with powerful reasoning capabilities, LLMs still need
to be fine-tuned to adapt to specific domains to achieve the best results. In
this paper, we propose to select the most informative data for fine-tuning,
thereby improving the efficiency of the fine-tuning process with comparative or
even better accuracy on the open-domain QA task. We present MinPrompt, a
minimal data augmentation framework for open-domain QA based on an approximate
graph algorithm and unsupervised question generation. We transform the raw text
into a graph structure to build connections between different factual
sentences, then apply graph algorithms to identify the minimal set of sentences
needed to cover the most information in the raw text. We then generate QA pairs
based on the identified sentence subset and train the model on the selected
sentences to obtain the final model. Empirical results on several benchmark
datasets and theoretical analysis show that MinPrompt is able to achieve
comparable or better results than baselines with a high degree of efficiency,
bringing improvements in F-1 scores by up to 27.5%.