The strive to make AI applications "safe" has led to the development of
safety-measures as the main or even sole normative requirement of their
permissible use. Similar can be attested to the latest version of chatbots,
such as chatGPT. In this view, if they are "safe", they are supposed to be
permissible to deploy. This approach, which we call "safety-normativity", is
rather limited in solving the emerging issues that chatGPT and other chatbots
have caused thus far. In answering this limitation, in this paper we argue for
limiting chatbots in the range of topics they can chat about according to the
normative concept of appropriateness. We argue that rather than looking for
"safety" in a chatbot's utterances to determine what they may and may not say,
we ought to assess those utterances according to three forms of
appropriateness: technical-discursive, social, and moral. We then spell out
what requirements for chatbots follow from these forms of appropriateness to
avoid the limits of previous accounts: positionality, acceptability, and value
alignment (PAVA). With these in mind, we may be able to determine what a
chatbot may and may not say. Lastly, one initial suggestion is to use challenge
sets, specifically designed for appropriateness, as a validation method.

该论文探讨聊天机器人的安全问题，提出了一种基于适当性概念的限制方法，包括技术 - 话语、社交和道德上的适当性，并提出了聊天机器人要满足的三个要求：立场性，可接受性和价值取向一致性（PAVA），同时建议使用适当性挑战集作为验证方法。

恰当性是您所需的一切！

Appropriateness is all you need!

Question answering (QA) models for reading comprehension have achieved
human-level accuracy on in-distribution test sets. However, they have been
demonstrated to lack robustness to challenge sets, whose distribution is
different from that of training sets. Existing data augmentation methods
mitigate this problem by simply augmenting training sets with synthetic
examples sampled from the same distribution as the challenge sets. However,
these methods assume that the distribution of a challenge set is known a
priori, making them less applicable to unseen challenge sets. In this study, we
focus on question-answer pair generation (QAG) to mitigate this problem. While
most existing QAG methods aim to improve the quality of synthetic examples, we
conjecture that diversity-promoting QAG can mitigate the sparsity of training
sets and lead to better robustness. We present a variational QAG model that
generates multiple diverse QA pairs from a paragraph. Our experiments show that
our method can improve the accuracy of 12 challenge sets, as well as the
in-distribution accuracy. Our code and data are available at
this https URL

通过提出多样性 QAG 模型，增强训练集连贯性并提高 QA 模型的鲁棒性，从而提高了在特定问题集中的准确性。