Existing works examining Vision Language Models (VLMs) for social biases
predominantly focus on a limited set of documented bias associations, such as
gender:profession or race:crime. This narrow scope often overlooks a vast range
of unexamined implicit associations, restricting the identification and, hence,
mitigation of such biases. We address this gap by probing VLMs to (1) uncover
hidden, implicit associations across 9 bias dimensions. We systematically
explore diverse input and output modalities and (2) demonstrate how biased
associations vary in their negativity, toxicity, and extremity. Our work (3)
identifies subtle and extreme biases that are typically not recognized by
existing methodologies. We make the Dataset of retrieved associations, (Dora),
publicly available here this https URL

通过检测视觉语言模型（VLMs）上的隐藏、隐含性关联，系统地探索多元的输入和输出模式，揭示存在于九个偏见维度上的细微和极端偏见，从而填补了现有方法的不足，发现通常不被现有方法识别到的隐秘和极端偏见，同时公开了关联数据集（Dora）。

BiasDora：探索视觉语言模型中隐藏的偏见关联

BiasDora: Exploring Hidden Biased Associations in Vision-Language Models

Event Causality Identification (ECI) aims to identify causal relations
between events in unstructured texts. This is a very challenging task, because
causal relations are usually expressed by implicit associations between events.
Existing methods usually capture such associations by directly modeling the
texts with pre-trained language models, which underestimate two kinds of
semantic structures vital to the ECI task, namely, event-centric structure and
event-associated structure. The former includes important semantic elements
related to the events to describe them more precisely, while the latter
contains semantic paths between two events to provide possible supports for
ECI. In this paper, we study the implicit associations between events by
modeling the above explicit semantic structures, and propose a Semantic
Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator
to integrate the event-centric structure information, and employs an LSTM-based
path aggregator to capture the event-associated structure information between
two events. Experimental results on three widely used datasets show that SemSIn
achieves significant improvements over baseline methods.

本文提出了一种名为 SemSIn 的模型，通过模拟事件中心结构和事件关联结构之间的语义关系，更准确地识别事件之间的因果关系，并在三个广泛使用的数据集上取得了显着的性能提升。