3 accepted papers
This position paper argues that vision-language model (VLM) benchmarks for causal reasoning rely on two under-examined assumptions. First, benchmarks presuppose temporal constitution, the understanding of time as the medium through which causes produce effects, without testing it as a prerequisite. …
Vision-language models (VLMs) generate fluent causal explanations for visual scenes, but does this fluency reflect genuine structural understanding? We address this question through a dual-probe methodology that isolates plausibility from faithfulness. The Text-Only Probe measures linguistic quality…
Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of…