| Fact Knowledge Stability Benchmark |
| Creative Commons Attribution 4.0 International (CC BY 4.0) |
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| You are free to share and adapt this material for any purpose, including |
| commercially, provided you give appropriate credit, link to the licence, and |
| indicate if changes were made. |
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| Full text: https://creativecommons.org/licenses/by/4.0/legalcode |
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| UPSTREAM SOURCES AND THEIR TERMS |
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| The facts in this benchmark are derived from the datasets below. Their terms |
| continue to apply to the derived material; CC BY 4.0 was chosen because it is |
| compatible with all of them. Please cite the original datasets alongside this |
| one. |
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| CounterFact MIT License |
| Meng et al., "Locating and Editing Factual Associations in GPT" (2022) |
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| LAMA / T-REx CC BY 4.0 |
| Petroni et al., "Language Models as Knowledge Bases?" (2019) |
| ElSahar et al., "T-REx: A Large Scale Alignment of Natural Language with |
| Knowledge Base Triples" (2018) |
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| LAMA / Google-RE CC BY 4.0 (per the LAMA distribution) |
| LAMA / ConceptNet CC BY-SA 4.0 (ConceptNet 5) |
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| PopQA MIT License |
| Mallen et al., "When Not to Trust Language Models" (2023) |
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| Wikidata CC0 1.0 (entity identifiers and aliases) |
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| The T-REx and Wikidata5M subsets present in the candidate pool were dropped |
| before benchmark selection and are not represented in the distributed files. |
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| The code under runner/ and metrics/ is released under the MIT License. |
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