Existing knowledge-enhanced methods have achieved remarkable results in
certain QA tasks via obtaining diverse knowledge from different knowledge
bases. However, limited by the properties of retrieved knowledge, they still
have trouble benefiting from both the knowledge relevance and distinguishment
simultaneously. To address the challenge, we propose CPACE, a Concept-centric
Prompt-bAsed Contrastive Explanation Generation model, which aims to convert
obtained symbolic knowledge into a contrastive explanation for better
distinguishing the differences among given candidates. Firstly, following
previous works, we retrieve different types of symbolic knowledge with a
concept-centric knowledge extraction module. After that, we generate
corresponding contrastive explanations using acquired symbolic knowledge and
explanation prompts as guidance for better modeling the knowledge
distinguishment and interpretability. Finally, we regard the generated
contrastive explanation as external knowledge for downstream task enhancement.
We conduct a series of experiments on three widely-used question-answering
datasets: CSQA, QASC, and OBQA. Experimental results demonstrate that with the
help of generated contrastive explanation, our CPACE model achieves new SOTA on
CSQA (89.8% on the testing set, 0.9% higher than human performance), and gains
impressive improvement on QASC and OBQA (4.2% and 3.5%, respectively).

使用 CPACE 模型和符号知识和解释提示来生成对比性解释，从而帮助区分问题答案，提高知识增强型方法在问答任务中的表现。

提出撰写对比解释作为常识问题回答知识的先验鉴别方法

Distinguish Before Answer: Generating Contrastive Explanation as  Knowledge for Commonsense Question Answering

Current interpretability methods focus on explaining a particular model's
decision through present input features. Such methods do not inform the user of
the sufficient conditions that alter these decisions when they are not
desirable. Contrastive explanations circumvent this problem by providing
explanations of the form "If the feature $X>x$, the output $Y$ would be
different''. While different approaches are developed to find contrasts; these
methods do not all deal with mutability and attainability constraints.
In this work, we present a novel approach to locally contrast the prediction
of any classifier. Our Contrastive Entropy-based explanation method, CEnt,
approximates a model locally by a decision tree to compute entropy information
of different feature splits. A graph, G, is then built where contrast nodes are
found through a one-to-many shortest path search. Contrastive examples are
generated from the shortest path to reflect feature splits that alter model
decisions while maintaining lower entropy. We perform local sampling on
manifold-like distances computed by variational auto-encoders to reflect data
density. CEnt is the first non-gradient-based contrastive method generating
diverse counterfactuals that do not necessarily exist in the training data
while satisfying immutability (ex. race) and semi-immutability (ex. age can
only change in an increasing direction). Empirical evaluation on four
real-world numerical datasets demonstrates the ability of CEnt in generating
counterfactuals that achieve better proximity rates than existing methods
without compromising latency, feasibility, and attainability. We further extend
CEnt to imagery data to derive visually appealing and useful contrasts between
class labels on MNIST and Fashion MNIST datasets. Finally, we show how CEnt can
serve as a tool to detect vulnerabilities of textual classifiers.

本论文提出了一种基于决策树、熵、对抗性生成模型等方法的可解释性算法 CEnt，能够生成种类多样性的反事实证明，不但在可行性、达成性等方面较现有方法表现更好，并且在 MNIST、Fashion MNIST 等数据集上进一步推广并成功用于文本分类器缺陷检测。

CEnt：一种基于熵的模型无关可解释性框架，用于对比分类器的决策

CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions

Contrastive explanation methods go beyond transparency and address the
contrastive aspect of explanations. Such explanations are emerging as an
attractive option to provide actionable change to scenarios adversely impacted
by classifiers' decisions. However, their extension to textual data is
under-explored and there is little investigation on their vulnerabilities and
limitations.
This work motivates textual counterfactuals by laying the ground for a novel
evaluation scheme inspired by the faithfulness of explanations. Accordingly, we
extend the computation of three metrics, proximity,connectedness and stability,
to textual data and we benchmark two successful contrastive methods, POLYJUICE
and MiCE, on our suggested metrics. Experiments on sentiment analysis data show
that the connectedness of counterfactuals to their original counterparts is not
obvious in both models. More interestingly, the generated contrastive texts are
more attainable with POLYJUICE which highlights the significance of latent
representations in counterfactual search. Finally, we perform the first
semantic adversarial attack on textual recourse methods. The results
demonstrate the robustness of POLYJUICE and the role that latent input
representations play in robustness and reliability.

本文介绍了一种通过对比解释方法，以可解释性和新建议形式提高分类器预测的可行性。该方法在文本数据上的应用情况得到了分析和评估，以及对研究所提供的度量标准的可靠性进行了初步的验证。

超出模型可解释性：对比文本解释的忠实度和对抗鲁棒性

Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations

This paper presents a model of contrastive explanation using structural
casual models. The topic of causal explanation in artificial intelligence has
gathered interest in recent years as researchers and practitioners aim to
increase trust and understanding of intelligent decision-making. While
different sub-fields of artificial intelligence have looked into this problem
with a sub-field-specific view, there are few models that aim to capture
explanation more generally. One general model is based on structural causal
models. It defines an explanation as a fact that, if found to be true, would
constitute an actual cause of a specific event. However, research in philosophy
and social sciences shows that explanations are contrastive: that is, when
people ask for an explanation of an event -- the fact -- they (sometimes
implicitly) are asking for an explanation relative to some contrast case; that
is, "Why P rather than Q?". In this paper, we extend the structural causal
model approach to define two complementary notions of contrastive explanation,
and demonstrate them on two classical problems in artificial intelligence:
classification and planning. We believe that this model can help researchers in
subfields of artificial intelligence to better understand contrastive
explanation.

本文提出了一种使用结构因果模型的对比解释模型，将其应用于分类和规划两个经典问题，以帮助人们更好地理解人工智能中的对比解释。