Human communication routinely relies on plural predication, and plural sentences are often ambiguous (see, e.g., Scha, 1984; Dalrymple et al., 1998a, to name a few). Building on extensive theoretical and experimental work in linguistics and philosophy, we ask whether large language models (LLMs) exhibit the same interpretive biases that humans show when resolving plural ambiguity. We focus on two lexical factors: (i) the collective bias of certain predicates (e.g., size/shape adjectives) and (ii) the symmetry bias of predicates. To probe these tendencies, we apply two complementary methods to premise–hypothesis pairs: an embedding-based heuristic using OpenAI’s text-embedding-3-large/small (OpenAI, 2024, 2025) with cosine similarity, and supervised NLI models (bart-large-mnli, roberta-large-mnli) (Lewis et al., 2020; Liu et al., 2019; Williams et al., 2018a; Facebook AI, 2024b,a) that yield asymmetric, calibrated entailment probabilities. Results show partial sensitivity to predicate-level distinctions, but neither method reproduces the robust human pattern, where neutral predicates favor entailment and strongly non-symmetric predicates disfavor it. These findings highlight both the potential and the limits of current LLMs: as cognitive models, they fall short of capturing human-like interpretive biases; as engineering systems, their representations of plural semantics remain unstable for tasks requiring precise entailment.