Machine Reading Comprehension (MRC) poses a significant challenge in the
field of Natural Language Processing (NLP). While mainstream MRC methods
predominantly leverage extractive strategies using encoder-only models such as
BERT, generative approaches face the issue of out-of-control generation -- a
critical problem where answers generated are often incorrect, irrelevant, or
unfaithful to the source text. To address these limitations in generative
models for MRC, we introduce the Question-Attended Span Extraction (QASE)
module. Integrated during the fine-tuning phase of pre-trained generative
language models (PLMs), QASE significantly enhances their performance, allowing
them to surpass the extractive capabilities of advanced Large Language Models
(LLMs) such as GPT-4. Notably, these gains in performance do not come with an
increase in computational demands. The efficacy of the QASE module has been
rigorously tested across various datasets, consistently achieving or even
surpassing state-of-the-art (SOTA) results.

在自然语言处理领域，机器阅读理解（MRC）存在着重大挑战。尽管主流的 MRC 方法主要使用仅编码器模型（如 BERT）利用抽取策略，但生成式方法面临着超出控制的生成问题 —— 这是一个关键问题，生成的答案经常是不正确、不相关或与源文本不一致。为了解决生成式 MRC 模型的这些局限性，我们引入了 Question-Attended Span Extraction（QASE）模块。在经过预训练的生成式语言模型（PLM）的微调阶段集成 QASE 模块，显著提高了它们的性能，使它们能够超越像 GPT-4 这样的先进大型语言模型（LLMs）的抽取能力。值得注意的是，这种性能提升并没有增加计算需求。QASE 模块的有效性已经在各种数据集上进行了严格的测试，始终实现甚至超越最先进的结果（SOTA）。