ClbQs

ClbQs is a benchmark framework for evaluating AI systems on questions that require multi-step reasoning across diverse domains. The term denotes a family of clustered question sets designed to test generalization and problem-solving beyond single-fact recall. Each collection is organized into clusters that target specific domains or reasoning patterns.

Structure and content: Within a cluster, questions are grouped by topic and ordered by difficulty. Many items require chain-of-thought-style justification, while others are standalone factual questions. Each item includes a reference answer and, when available, a short rationale describing the required reasoning steps.

Creation and curation: ClbQs is typically assembled from a mix of public-domain sources and vetted textbooks and articles. Human reviewers perform quality control to ensure accuracy and minimize leakage between training and test sets. Clusters are balanced to cover diverse domains and reasoning types, and metadata tracks source, difficulty, and cognitive requirements.

Evaluation and usage: Researchers benchmark QA systems with ClbQs, reporting metrics such as accuracy and, when applicable, the quality of reasoning traces. The framework supports multiple-choice and free-response formats and is often used with retrieval-augmented generation and chain-of-thought prompting.

History and impact: The concept emerged in AI evaluation literature in the early 2020s and has since been adopted by research groups to assess cross-domain reasoning and robustness. It is cited in studies comparing model capabilities and in educational contexts for evaluating problem sets.

Limitations and related benchmarks: Like other datasets, ClbQs faces biases, coverage gaps, and risks of overfitting to cluster structures. It is commonly used alongside benchmarks such as SQuAD, HotpotQA, and BigBench.