PopQA
PopQA is an entity-centric open-domain question-answering dataset consisting of 14,000 QA pairs designed to evaluate language models' ability to memorize and recall factual knowledge across entities with varying popularity levels. The dataset probes both parametric memory (stored in model parameters) and non-parametric memory effectiveness, with questions covering 16 diverse relationship types from Wikidata converted to natural language using templates. Created by sampling knowledge triples from Wikidata and converting them to natural language questions, focusing on long-tail entities to understand LMs' strengths and limitations in memorizing factual knowledge.
Granite 3.3 8B Base from IBM currently leads the PopQA leaderboard with a score of 0.262 across 3 evaluated AI models.
Granite 3.3 8B Base leads with 26.2%, followed by Granite 3.3 8B Instruct at 26.2% and IBM Granite 4.0 Tiny Preview at 22.9%.
Progress Over Time
Interactive timeline showing model performance evolution on PopQA
PopQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 8B | — | — | |||
| 1 | 8B | 128K | $0.50 / $0.50 | |||
| 3 | 7B | — | — |
FAQ
Common questions about PopQA.
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