OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection

Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications. While zero-shot OOD detection, which requires no training on in-distribution (ID) data, has become feasible with the emergence of vision-language models like CLIP, existing methods primarily focus on semantic matching and fail to fully capture distributional discrepancies. To address these limitations, we propose OT-DETECTOR, a novel framework that employs Optimal Transport (OT) to quantify both semantic and distributional discrepancies between test samples and ID labels. Specifically, we introduce cross-modal transport mass and transport cost as semantic-wise and distribution-wise OOD scores, respectively, enabling more robust detection of OOD samples. Additionally, we present a semantic-aware content refinement (SaCR) module, which utilizes semantic cues from ID labels to amplify the distributional discrepancy between ID and hard OOD samples. Extensive experiments on several benchmarks demonstrate that OT-DETECTOR achieves state-of-the-art performance across various OOD detection tasks, particularly in challenging hard-OOD scenarios.

On Detecting Cherry-picking in News Coverage Using Large Language Models

Cherry-picking refers to the deliberate selection of evidence or facts that favor a particular viewpoint while ignoring or distorting evidence that supports an opposing perspective. Manually identifying instances of cherry-picked statements in news stories can be challenging, particularly when the opposing viewpoint's story is absent. This study introduces Cherry, an innovative approach for automatically detecting cherry-picked statements in news articles by finding missing important statements in the target news story. Cherry utilizes the analysis of news coverage from multiple sources to identify instances of cherry-picking. Our approach relies on language models that consider contextual information from other news sources to classify statements based on their importance to the event covered in the target news story. Furthermore, this research introduces a novel dataset specifically designed for cherry-picking detection, which was used to train and evaluate the performance of the models. Our best performing model achieves an F-1 score of about %89 in detecting important statements when tested on unseen set of news stories. Moreover, results show the importance incorporating external knowledge from alternative unbiased narratives when assessing a statement's importance.