Datasets:
Modalities:
Text
Formats:
json
Languages:
English
Size:
1K - 10K
Tags:
benchmark
llm-evaluation
reasoning
knowledge-representation
open-world-assumption
closed-world-assumption
License:
Add NeSy 2026 badge
Browse files
README.md
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# ClosureBench
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ClosureBench is a controlled benchmark for evaluating if LLMs
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respect explicit semantic contracts about missing information. It tests whether
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models distinguish absence-as-unknown, absence-as-false, and
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# ClosureBench
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[](https://nesy-ai.org/conferences/nesy-2026)
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ClosureBench is a controlled benchmark for evaluating if LLMs
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respect explicit semantic contracts about missing information. It tests whether
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models distinguish absence-as-unknown, absence-as-false, and
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