Computer Science > Computation and Language
arXiv:2509.07968 (cs)
[Submitted on 9 Sep 2025 (v1), last revised 10 Mar 2026 (this version, v2)]
Title:SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
View a PDF of the paper titled SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge, by Lukas Haas and 4 other authors
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Abstract:We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitations in OpenAI's benchmark, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created through a rigorous multi-stage filtering process involving de-duplication, topic balancing, and source reconciliation to produce a more reliable and challenging evaluation set, alongside improvements in the autorater prompt. On this new benchmark, Gemini 2.5 Pro achieves a state-of-the-art F1-score of 55.6, outperforming other frontier models, including GPT-5. This work provides the research community with a higher-fidelity tool to track genuine progress in parametric model factuality and to mitigate hallucinations. The benchmark dataset, evaluation code, and leaderboard are available at: this https URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2509.07968 [cs.CL] |
| (or arXiv:2509.07968v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.07968
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arXiv-issued DOI via DataCite
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Submission history
From: Lukas Haas [view email][v1] Tue, 9 Sep 2025 17:53:58 UTC (414 KB)
[v2] Tue, 10 Mar 2026 12:28:26 UTC (410 KB)
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View a PDF of the paper titled SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge, by Lukas Haas and 4 other authors
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