It is suggested that robust meme understanding requires multimodal training, varies substantially across semantic categories, and remains sensitive to over-specialization when models are fine-tuned on individual datasets rather than trained in a unified setting.
Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, A. Hasnat et al.· arXiv.org· 5 citations
A semantic correctness taxonomy is introduced that assigns open-ended answers to eight ordered classes, separating verbose-but-correct answers from those contaminated by hallucinated content and CAP (Context-Aware Precision), a reference-based metric that scores question-conditioned statements using bidirectional NLI.
Elitsa Yotkova, Violeta Kastreva, Petar Velkov et al.· 0 citations
FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task tests whether systems can select the correct answer to finance questions involving domain terminology, numerical interpretation, and conceptual financial reasoning across languages...
Zhuohan Xie, Yu-Yang Dai, R. Elbadry et al.· arXiv.org· 1 citation
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