This study aims to test how large language models (LLMs) understand gradable adjectives and whether their understanding compares with humans, under the framework of formal semantics. We introduce a diagnostic dataset, referred to as the Modifier-Adjective Scale Probe (MASP), to evaluate how well LLMs understand a gradable adjective (e.g., long) when the adjective is combined with one modifier (e.g., very long or slightly long, a condition referred to as degree modification) or is further negated (e.g., very not long and not very long, a condition referred to as compositional negation). The dataset consists of over 80,000 natural language inference questions in both Chinese and English. We apply the MASP dataset to test both humans and 11 popular LLMs, including GPT-4 and Gemini-2.0-Flash. The results show that most LLMs can correctly understand whether a modifier boosts (e.g., very) an adjective. However, they fail to understand the modifiers that weaken the degree and the negation forms of modifiers. Furthermore, we parameterize the human and LLM behavior, and find that the judgment patterns of LLMs differ from humans especially in the Chinese tests. These findings suggest that LLMs are still not well aligned with humans in terms of the interpretation of simple adjective phrases, and MASP provides a new approach to quantify the interpretation of adjective phrases in LLMs.