# Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers

arXiv: https://arxiv.org/abs/1806.00354 · HTML: https://mlsanity.com/paper/1806.00354/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers](https://mlsanity.com/paper/2212.08700/) (2022)
- [Differentiable Fuzzy Inference Layer: A Monotone, Compositional Ordinal Reasoning Head for Large Language Models](https://mlsanity.com/paper/2609.26113/) (2026)

## Abstract (arXiv)

We study the role of linguistic context in predicting quantifiers (`few', `all'). We collect crowdsourced data from human participants and test various models in a local (single-sentence) and a global context (multi-sentence) condition. Models significantly out-perform humans in the former setting and are only slightly better in the latter. While human performance improves with more linguistic context (especially on proportional quantifiers), model performance suffers. Models are very effective in exploiting lexical and morpho-syntactic patterns; humans are better at genuinely understanding the meaning of the (global) context.