Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication?
Large Language Models (LLMs) are increasingly being used to summarize and verify claims in critical sources that inform consequential decisions. Such claims are full of plural definites. If three of five patients improved after taking drug A, the claim the patients improved after drug A is not true, since two did not, yet its negation, the patients didn't improve after drug A, is not true either, since three did. In formal semantics, this truth-value gap is known as the Homogeneity Gap: both the plural definite and its negation are neither true nor false when the property holds for only a fraction of the plural referent. A model that cannot represent this gap turns mixed evidence into a c...
| Authors | Duaa Nakshbandi, Zhiyu An, Yiping Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 October 2026 |
| Last updated at source | 14 October 2026 |
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