D. Dimitrov

Overview of the ImageCLEF 2026 Task on Multimodal Reasoning

MemeLens: Multilingual Multitask VLMs for Memes

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.

How Correct Is Your Answer? A Semantic Correctness Framework for Open QA Evaluation

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.

Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering

FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence over multilingual evidence using document retrieval-augmented generation, cross-lingual evidence handling, structured prompting, answer compression, and validation strategies.

Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering

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...