Explaining an artwork requires knowledge beyond visual recognition, including facts about its creator, subject, material, style, collection, and historical context. Knowledge graphs can provide such evidence, but artwork KGs are incomplete, especially for newly encountered artworks. We introduce ArtKGC, a leakage-controlled multimodal benchmark and evaluation protocol for artwork knowledge graph completion. ArtKGC links artworks to Wikidata facts, Wikipedia descriptions, and visual evidence while controlling ambiguous mappings, graph leakage, and descriptions that directly reveal held-out answers. We show that graph-only models are weak for unseen artworks, raw text can inflate evaluation through answer leakage, and visual evidence is strongly relation-dependent. As an interpretable diagnostic baseline, we use a validation-calibrated reranker that combines structural, sentence-redacted textual, and visual evidence. Experiments show improved completion in the primary inductive-artwork setting and modest downstream evidence gains for artwork QA and explanation diagnostics.

Many artwork questions begin with vision but cannot be answered by visual recognition alone. A model may need to connect an artwork to its creator, depicted subject, collection, or location, and then follow multiple facts to reach the answer. We introduce ArtHopQA, a multimodal multi-hop question answering benchmark for artwork understanding, containing 4,885 image-grounded questions paired with supporting fact chains. Experiments with open and API large vision-language models show a large gap: even the strongest open model reaches only 36.87% accuracy when given the image and title, while gold-path performance reaches 97.77% to 99.80% when the supporting fact chain is provided. Text-only language models exhibit a similar gap, showing that the difficulty is not confined to visual input. Further analysis reveals high-prior geographic guesses, inconsistent answers under image-title conflict, and limited recovery of intermediate entities even when final answers are correct.

Retrieval Augmented Generation (RAG) complements the knowledge of Large Language Models (LLMs) by leveraging external information to enhance response accuracy for queries. This approach is widely applied in several fields by taking its advantage of injecting the most up-to-date information, and researchers are focusing on understanding and improving this aspect to unlock the full potential of RAG in such high-stakes applications.However, despite the potential of RAG to address these needs, the mechanisms behind the confidence levels of its outputs remain underexplored.Our study focuses on the impact of RAG, specifically examining whether RAG increases the confidence of LLM outputs in the medical domain.We conduct this analysis across various configurations and models.We evaluate confidence by treating the model’s predicted probability as its output and calculating several evaluation metrics which include calibration error method, entropy, best probability, and accuracy.Experimental results across multiple datasets confirmed that certain models possess the capability to judge for themselves whether an inserted document relates to the correct answer. These results suggest that evaluating models based on their output probabilities determine whether they function as generators in the RAG framework.Our approach allows to evaluate whether the models handle retrieved documents.