To address the challenges of out-of-control generation in generative models
for machine reading comprehension (MRC), we introduce the Question-Attended
Span Extraction (QASE) module. Integrated during the fine-tuning of pre-trained
generative language models (PLMs), QASE enables these PLMs to match SOTA
extractive methods and outperform leading LLMs like GPT-4 in MRC tasks, without
significant increases in computational costs.

針對生成模型在機器閱讀理解中失控的問題，我們引入了問題關注的範圍提取 (QASE) 模塊。在預訓練生成語言模型 (PLMs) 的微調過程中，QASE 使得這些 PLMs 能夠匹配 SOTA 的範圍提取方法，在 MRC 任務中表現優於領先的 LLMs 如 GPT-4，並且不會增加顯著的計算成本。

QASE 增强的 PLM：提升 MRC 中的文本生成控制

QASE Enhanced PLMs: Improved Control in Text Generation for MRC

Machine reading comprehension (MRC) is an important area of conversation
agents and draws a lot of attention. However, there is a notable limitation to
current MRC benchmarks: The labeled answers are mostly either spans extracted
from the target corpus or the choices of the given candidates, ignoring the
natural aspect of high-quality responses. As a result, MRC models trained on
these datasets can not generate human-like responses in real QA scenarios. To
this end, we construct a new dataset called Penguin to promote the research of
MRC, providing a training and test bed for natural response generation to real
scenarios. Concretely, Penguin consists of 200k training data with high-quality
fluent, and well-informed responses. Penguin is the first benchmark towards
natural response generation in Chinese MRC on a relatively large scale. To
address the challenges in Penguin, we develop two strong baselines: end-to-end
and two-stage frameworks. Following that, we further design Prompt-BART:
fine-tuning the pre-trained generative language models with a mixture of prefix
prompts in Penguin. Extensive experiments validated the effectiveness of this
design.

该研究构建了一个名为 Penguin 的新数据集，用于促进自然反应生成的 MRC 研究，并提供训练和测试场地。研究人员通过对机器阅读理解模型的测试，采用基于前缀提示的模型，证明了该模型的有效性。