A trending paradigm for multiple-choice question answering (MCQA) is using a
text-to-text framework. By unifying data in different tasks into a single
text-to-text format, it trains a generative encoder-decoder model which is both
powerful and universal. However, a side effect of twisting a generation target
to fit the classification nature of MCQA is the under-utilization of the
decoder and the knowledge that can be decoded. To exploit the generation
capability and underlying knowledge of a pre-trained encoder-decoder model, in
this paper, we propose a generation-enhanced MCQA model named GenMC. It
generates a clue from the question and then leverages the clue to enhance a
reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.

该论文介绍了一种基于文本生成模型的、通过生成提示信息来提高多项选择题的准确性的算法 GenMC，该算法在多项选择题上的表现优于传统文本生成模型。

线索优先：增强型多项选择题解答

Clues Before Answers: Generation-Enhanced Multiple-Choice QA

Neural networks have recently achieved human-level performance on various
challenging natural language processing (NLP) tasks, but it is notoriously
difficult to understand why a neural network produced a particular prediction.
In this paper, we leverage the text-to-text framework proposed by Raffel et
al.(2019) to train language models to output a natural text explanation
alongside their prediction. Crucially, this requires no modifications to the
loss function or training and decoding procedures -- we simply train the model
to output the explanation after generating the (natural text) prediction. We
show that this approach not only obtains state-of-the-art results on
explainability benchmarks, but also permits learning from a limited set of
labeled explanations and transferring rationalization abilities across
datasets. To facilitate reproducibility and future work, we release our code
use to train the models.

本文使用文本到文本的框架，训练语言模型在生成自然文本预测的同时输出自然文本解释，实现了在可解释性基准测试上的最先进结果，并且允许跨数据集传递来自有限数量标记解释的理解能力。