When Do LLMs Say “Some”? An Investigation of Scalar Implicature and Politeness Mitigation in Large Language Models

Scalar implicatures link semantic meaning to pragmatic reasoning: although some is logically compatible with all, listeners often enrich some to “some but not all.” Yet some also functions as a politeness hedge that mitigates face-threatening acts, potentially canceling the usual “not all” inference. This study investigates whether large language models (LLMs) exhibit the same context-sensitive trade-off between informativeness and politeness as humans, and whether chain-of-thought (CoT) optimization yields more human-like pragmatic flexibility. Mandarin-speaking participants completed parallel production and judgment tasks that manipulated face threat (face-threatening vs. neutral) and factual state ( All vs. Most ). In Experiment 1, participants completed sentences among seven quantifiers, including some in Experiment 2, they evaluated the acceptability of under-informative some statements versus fully informative factual statements, with reaction times recorded. Three LLMs (DeepSeek-V3.2, DeepSeek-R1, GPT-4o) were tested with identical materials. Humans showed a robust informativeness–politeness reweighting: face threat increased production and acceptance of some while sharply reducing acceptance of blunt, fully informative statements. LLMs generally captured the face-based licensing of vagueness but did not reliably penalize socially costly maximal informativeness. The CoT-optimized model showed greater context sensitivity and more human-like distributional patterns than the non-CoT models. Together, these findings are consistent with partial alignment with human mitigation but incomplete social-norm calibration, with CoT optimization offering a modest reduction in the mismatch.