Understanding interpersonal communication requires, in part, understanding
the social context and norms in which a message is said. However, current
methods for identifying offensive content in such communication largely operate
independent of context, with only a few approaches considering community norms
or prior conversation as context. Here, we introduce a new approach to
identifying inappropriate communication by explicitly modeling the social
relationship between the individuals. We introduce a new dataset of
contextually-situated judgments of appropriateness and show that large language
models can readily incorporate relationship information to accurately identify
appropriateness in a given context. Using data from online conversations and
movie dialogues, we provide insight into how the relationships themselves
function as implicit norms and quantify the degree to which context-sensitivity
is needed in different conversation settings. Further, we also demonstrate that
contextual-appropriateness judgments are predictive of other social factors
expressed in language such as condescension and politeness.

通过显式地对个体之间的社会关系进行建模，我们引入了一种鉴别不适当交流的新方法，并且证明了大型语言模型可以在给定的上下文中准确地识别适当性。此外，我们还证明了上下文适宜性判断对于表达出的其他社会因素（如居高临下和礼貌）具有预测性。

您的配偶需要专业帮助：通过建模社会关系确定消息的上下文适宜性

Your spouse needs professional help: Determining the Contextual  Appropriateness of Messages through Modeling Social Relationships

This paper introduces epistemic graphs as a generalization of the epistemic
approach to probabilistic argumentation. In these graphs, an argument can be
believed or disbelieved up to a given degree, thus providing a more
fine--grained alternative to the standard Dung's approaches when it comes to
determining the status of a given argument. Furthermore, the flexibility of the
epistemic approach allows us to both model the rationale behind the existing
semantics as well as completely deviate from them when required. Epistemic
graphs can model both attack and support as well as relations that are neither
support nor attack. The way other arguments influence a given argument is
expressed by the epistemic constraints that can restrict the belief we have in
an argument with a varying degree of specificity. The fact that we can specify
the rules under which arguments should be evaluated and we can include
constraints between unrelated arguments permits the framework to be more
context--sensitive. It also allows for better modelling of imperfect agents,
which can be important in multi--agent applications.

该论文介绍了认识图作为概率论证的普遍化，它可以更好地建模攻击和支持关系，包括不支持和不攻击关系，同时允许更多情境敏感性和完善代理建模。

用认知图表示和推理论证的正面和负面影响

Epistemic Graphs for Representing and Reasoning with Positive and  Negative Influences of Arguments

Sensory inference under conditions of uncertainty is a major problem in both
machine learning and computational neuroscience. An important but poorly
understood aspect of sensory processing is the role of active sensing. Here, we
present a Bayes-optimal inference and control framework for active sensing,
C-DAC (Context-Dependent Active Controller). Unlike previously proposed
algorithms that optimize abstract statistical objectives such as information
maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy
[Najemnik & Geisler, 2005], our active sensing model directly minimizes a
combination of behavioral costs, such as temporal delay, response error, and
effort. We simulate these algorithms on a simple visual search task to
illustrate scenarios in which context-sensitivity is particularly beneficial
and optimization with respect to generic statistical objectives particularly
inadequate. Motivated by the geometric properties of the C-DAC policy, we
present both parametric and non-parametric approximations, which retain
context-sensitivity while significantly reducing computational complexity.
These approximations enable us to investigate the more complex problem
involving peripheral vision, and we notice that the difference between C-DAC
and statistical policies becomes even more evident in this scenario.

本研究提出了一种基于贝叶斯最优推理和控制框架的主动感知算法，C-DAC，该算法通过直接最小化行为成本，例如时间延迟、响应误差和努力来优化主动感知模型，从而实现了上下文关联的主动控制。我们在简单的视觉搜索任务上模拟这些算法，以说明情境敏感性特别有益的情况和相对于一般统计目标而言优化特别不足的情况。同时，为了解决复杂的问题，我们提出了参数化和非参数化的近似方法，这些近似方法保持上下文相关性同时显著减少计算复杂度。