<sub>The authors do not endorse any political, ideological, or moral position implied by the items or by model outputs. All entries are probes for factual and value-related reasoning and must not be used to profile real people or to justify harmful actions or decisions.</sub>
Profiling: Knowledge Graph-Based Dynamic Factuality Evaluation is a research benchmark for studying how large language models handle facts in a dynamic knowledge graph, including retrieving, updating, and verifying them under changing context.
- Goal: evaluate how well the LLM can reproduce verifiable facts and whether it maintains the integrity of its "beliefs" when the data set changes.
- Data source: Wiki, Gre
- Size: ≈19K multiple-choice questions
- Format: JSON
Tasks this dataset is suitable for:
- 🧠 Factual Knowledge Evaluation: Measuring factual accuracy and knowledge retrieval capabilities across domains (geography, history, science, culture).
- 🧩 Knowledge Graph Reasoning: Evaluating ability to infer relationships between entities and navigate structured knowledge.
Primary language of instructions/prompts: ru (Russian), en (English).
Example of a single instance:
{
"task": "What body of water is near the birthplace of Marcelo Romero?",
"option_1": "River Gironde",
"option_2": "Hamoaze",
"option_3": "River Plate",
"option_4": "Haring river",
"correct": 3,
"meta_difficulty": "medium",
"meta_type": "single",
"meta_origin": "wikidata"
}If you use LLM-Profiling in your work, please cite it as:
@misc{stonic_worldview_benchmark_2025,
title = {LLM-Profiling: A Worldview Benchmark for Large Language Models},
author = {Andrey Chetvergov — chetvergov-as@ranepa.ru
Rinat Sharafetdinov — sharafetdinov-rs@ranepa.ru
Stepan Ukolov — ukolov-sd@ranepa.ru
Timofei Sivoraksha — sivoraksha-ta@ranepa.ru
Alexander Evseev — aevseev-23-01@ranepa.ru
Danil Sazanakov — hdystasyfibkv@gmail.com
Sergey Bolovtsov — bolovtsov-sv@ranepa.ru},
year = {2025},
howpublished = {\url{https://huggingface.co/datasets/llmpass-ai/stonic_dataset}},
}
