wikipedia-16fbd615·4 events·first seen Aliases: Wikipedia
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.
A large-scale arXiv study audits political neutrality in Grokipedia—an encyclopedia generated by xAI's Grok LLM—versus Wikipedia, analyzing 1,394 article pairs about government members across nine ideology dimensions using four LLM judges (Grok, Claude, Mistral, DeepSeek). All four judges, including Grok itself, rate Grokipedia as less neutral than Wikipedia. The study finds Grokipedia favors economically right-wing politicians and penalizes socially liberal ones, while Wikipedia shows the opposite bias pattern, raising questions about whether LLM-generated content can deliver ideological neutrality.
Researchers introduce KDoS (Knowledge Distribution-optimized Synthesis), a framework that uses a three-stage feedback mechanism guided by 'knowledge density' to optimize the distribution of synthetic training data for LLMs. Rather than stopping at preset token counts or fixed ratios, KDoS dynamically adjusts synthesis to avoid sparse or redundant domain coverage. Experiments across Qwen, Ling, and LLaMA models (0.6B–16B parameters) on 1B–5B token scales show consistent improvements over baselines on six knowledge benchmarks. A key finding is that an optimal knowledge distribution exists and remains stable across model families and scales.
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.