LLMs over-certify negative answers from partial evidence, especially when completeness is implied rather than stated, and prompting mainly trades over-closure for under-closure.
Proceedings of the International Semantic Web Conference , pages =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models
LLMs over-certify negative answers from partial evidence, especially when completeness is implied rather than stated, and prompting mainly trades over-closure for under-closure.