Generative models have demonstrated human-level proficiency in various benchmarks across domains like programming, natural sciences, and general knowledge.

Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves responses that are factually correct but lack certain features such as clarity and relevance. This challenge is prevalent in formal Question-Answer (QA) settings such as press conferences in finance, politics, sports, and other domains, where subjective answers can obscure transparency. Despite this, there is a lack of manually annotated datasets for subjective features across multiple dimensions. To address this gap, we introduce SubjECTive-QA, a human annotated dataset on Earnings Call Transcripts' (ECTs) QA sessions as the answers given by company representatives are often open to subjective interpretations and scrutiny. The dataset includes 49, 446 annotations for long-form QA pairs across six features: Assertive, Cautious, Optimistic, Specific, Clear, and Relevant . These features are carefully selected to encompass the key attributes that reflect the tone of the answers provided during QA sessions across different domains. Our findings are that the best-performing Pre-trained Language Model (PLM), RoBERTa-base, has similar weighted F1 scores to Llama-3-70b-Chat on features with lower subjectivity, such as Relevant and Clear, with a mean difference of 2 .

Identity theft involving stolen driver's licenses creates lasting legal exposure unlike credit card fraud, as license numbers cannot be changed and require extensive cleanup efforts.

MIM to enhance the adversarial robustness of downstream models. It is important to highlight that our paper's focus is specifically on the adversarial robustness of ViTs. It is shown that our method can provide an effective defense against severe adversarial attacks. We propose two hypotheses for explaining the reason behind our method's effectiveness: (1) Given Figure 3 (a) shows the comparison between the results of noise being known and unknown. When the attacker can access the noise, our model's robust accuracy does not improve much as The results indicate that both proposed hypotheses are true.

The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data.

The keywords were extracted using the procedure described in SectionC. The restricted part of the Muharaf dataset has 428 images distributed under a proprietary license.