wikidata-279a31bd·2 events·first seen Aliases: Wikidata
Researchers introduce Kontrast, an automatic framework for detecting and categorizing knowledge inconsistencies between Wikipedia text, tables, and Wikidata knowledge graphs. The system uses Text-to-SPARQL and LLM reasoning to compare table-based answers against KG evidence, revealing conflicts, temporal mismatches, and structural gaps. Experiments on Table-QA datasets show cross-modal inconsistencies are common and informative, with implications for RAG pipelines and LLM pre-training data quality. Code and data are publicly released alongside a benchmark for future cross-modal consistency work.
WikiVQABench is a new human-curated VQA benchmark that requires external knowledge beyond visual perception, constructed by combining Wikipedia images, captions, and Wikidata structured knowledge with LLM-generated question candidates reviewed by human annotators. The benchmark evaluates knowledge-intensive reasoning in vision-language models, covering 15 VLMs ranging from 256M to 90B parameters. Accuracy spans 24.7% to 75.6%, indicating meaningful discrimination across model scales. The dataset and code are publicly released.