Generalized Quantifier

Generalized quantifiers, words like "some," "most," and "few," pose significant challenges for natural language understanding (NLU) models, despite their crucial role in expressing nuanced meaning. Current research focuses on developing benchmarks and methodologies to accurately assess and improve models' comprehension of these quantifiers, particularly in the context of fuzzy logic and pragmatic reasoning, often employing techniques from first-order logic and rational speech acts. These efforts are vital for advancing NLU capabilities in various applications, including human-robot interaction and improving the reliability of multilingual NLU benchmarks where quantifiers frequently contribute to errors. The ultimate goal is to build more robust and accurate NLU systems capable of handling the complexities of natural language quantification.