# Focus particles and scalar inferences across humans and language models

arXiv: https://arxiv.org/abs/2608.08227 · HTML: https://mlsanity.com/paper/2608.08227/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 9 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Optimizing Temperature for Language Models with Multi-Sample Inference](https://mlsanity.com/paper/2502.05234/) (2025)
- [Different types of syntactic agreement recruit the same units within large language models](https://mlsanity.com/paper/2512.03676/) (2025)
- [Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task](https://mlsanity.com/paper/2505.16164/) (2025)
- [The Necessity of Setting Temperature in LLM-as-a-Judge](https://mlsanity.com/paper/2603.28304/) (2026)
- [Modeling the human lexicon under temperature variations: linguistic factors, diversity and typicality in LLM word associations](https://mlsanity.com/paper/2603.18171/) (2026)

## Abstract (arXiv)

Focus particles such as "even" and "only" are central to formal semantic theories that posit structured representations over sets of alternatives. "Even" highlights unexpected or extreme alternatives, while "only" enforces exclusivity. If such scalar representations are robust and generalizable, they should give rise to consistent judgments across contexts and systems. In this work, we test whether humans and large language models (LLMs) construct stable scalar representations from sentences containing these particles. Using a dataset of approximately 100 items, participants and models were asked to make scalar judgments. Preliminary results suggest that similar outputs across humans and LLMs may arise from different underlying mechanisms.