Question generation has recently gained a lot of research interest,
especially with the advent of large language models. In and of itself, question
generation can be considered 'AI-hard', as there is a lack of unanimously
agreed sense of what makes a question 'good' or 'bad'. In this paper, we tackle
two fundamental problems in parallel: on one hand, we try to solve the scaling
problem, where question-generation and answering applications have to be
applied to a massive amount of text without ground truth labeling. The usual
approach to solve this problem is to either downsample or summarize. However,
there are critical risks of misinformation with these approaches. On the other
hand, and related to the misinformation problem, we try to solve the 'safety'
problem, as many public institutions rely on a much higher level of accuracy
for the content they provide. We introduce an adversarial approach to tackle
the question generation safety problem with scale. Specifically, we designed a
question-answering system that specifically prunes out unanswerable questions
that may be generated, and further increases the quality of the answers that
are generated. We build a production-ready, easily-plugged pipeline that can be
used on any given body of text, that is scalable and immune from generating any
hate speech, profanity, or misinformation. Based on the results, we are able to
generate more than six times the number of quality questions generated by the
abstractive approach, with a perceived quality being 44% higher, according to a
survey of 168 participants.

该论文介绍了一种采用对抗性方法的问题生成系统，旨在解决问题生成中的扩展性和安全性问题，并提出了一种问题 - 回答系统来剪枝不可回答的问题并提高答案质量，实现了高效、安全、准确、生产就绪的问答系统。

对抗性和安全缩放问题生成

Adversarial and Safely Scaled Question Generation

Recent theoretical works based on the neural tangent kernel (NTK) have shed
light on the optimization and generalization of over-parameterized networks,
and partially bridge the gap between their practical success and classical
learning theory. Especially, using the NTK-based approach, the following three
representative results were obtained: (1) A training error bound was derived to
show that networks can fit any finite training sample perfectly by reflecting a
tighter characterization of training speed depending on the data complexity.
(2) A generalization error bound invariant of network size was derived by using
a data-dependent complexity measure (CMD). It follows from this CMD bound that
networks can generalize arbitrary smooth functions. (3) A simple and analytic
kernel function was derived as indeed equivalent to a fully-trained network.
This kernel outperforms its corresponding network and the existing gold
standard, Random Forests, in few shot learning. For all of these results to
hold, the network scaling factor $\kappa$ should decrease w.r.t. sample size n.
In this case of decreasing $\kappa$, however, we prove that the aforementioned
results are surprisingly erroneous. It is because the output value of trained
network decreases to zero when $\kappa$ decreases w.r.t. n. To solve this
problem, we tighten key bounds by essentially removing $\kappa$-affected
values. Our tighter analysis resolves the scaling problem and enables the
validation of the original NTK-based results.

使用神经切比洛夫核方法，获得了网络训练误差上限、网络大小不变的泛化误差上限，以及一个简单且解析的核函数，能够优于相关网络，但需要注意网络缩放因子的问题。本文对原有方法进行修正，提出了更加严格的误差上限，解决了缩放问题。