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%.

该研究提出了一个基于最小数据增强框架的开放领域问答系统，通过使用近似图算法和无监督问题生成，选择最具信息量的数据集进行微调，从而在效率上实现可比较或更好的准确性，提高了 F-1 分数高达 27.5%。

MinPrompt: 基于图的极简提示数据增强用于少样本问题回答

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

Few-shot question answering (QA) aims at precisely discovering answers to a
set of questions from context passages while only a few training samples are
available. Although existing studies have made some progress and can usually
achieve proper results, they suffer from understanding deep semantics for
reasoning out the questions. In this paper, we develop Gotta, a Generative
prOmpT-based daTa Augmentation framework to mitigate the challenge above.
Inspired by the human reasoning process, we propose to integrate the cloze task
to enhance few-shot QA learning. Following the recent success of prompt-tuning,
we present the cloze task in the same format as the main QA task, allowing the
model to learn both tasks seamlessly together to fully take advantage of the
power of prompt-tuning. Extensive experiments on widely used benchmarks
demonstrate that Gotta consistently outperforms competitive baselines,
validating the effectiveness of our proposed prompt-tuning-based cloze task,
which not only fine-tunes language models but also learns to guide reasoning in
QA tasks. Further analysis shows that the prompt-based loss incorporates the
auxiliary task better than the multi-task loss, highlighting the strength of
prompt-tuning on the few-shot QA task.

本研究开发了 Gotta，一种基于生成式提示的的数据增强框架，旨在改善 few-shot question answering 学习中深层语义推理的挑战。研究表明，Gotta 始终优于竞争基线，验证了基于提示调整的背景填充任务的有效性，在 QA 任务中学习指导推理并提高语言模型扩展任务能力的优点。