Encyclo-K: A New Benchmark for Evaluating LLMs
Published:Dec 31, 2025 13:55
•1 min read
•ArXiv
Analysis
This paper introduces Encyclo-K, a novel benchmark for evaluating Large Language Models (LLMs). It addresses limitations of existing benchmarks by using knowledge statements as the core unit, dynamically composing questions from them. This approach aims to improve robustness against data contamination, assess multi-knowledge understanding, and reduce annotation costs. The results show that even advanced LLMs struggle with the benchmark, highlighting its effectiveness in challenging and differentiating model performance.
Key Takeaways
- •Encyclo-K is a statement-based benchmark for LLMs.
- •It addresses limitations of existing question-based benchmarks.
- •Questions are dynamically composed from knowledge statements.
- •Reduces vulnerability to data contamination and annotation costs.
- •Provides a challenging and discriminative evaluation of LLMs.
Reference
“Even the top-performing OpenAI-GPT-5.1 achieves only 62.07% accuracy, and model performance displays a clear gradient distribution.”