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# Is It \textit JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs

**Authors:** [William Sheffield](https://md.mova.elamur.ai/free/en/docs/papers/authors/william-sheffield/), Kanishka Misra, [Valentina Pyatkin](https://md.mova.elamur.ai/free/en/docs/papers/authors/valentina-pyatkin/) et al. · **Year:** 2025 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2025 **Link:** [aclanthology.org/2025.findings-acl.1117](https://aclanthology.org/2025.findings-acl.1117/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[**0** citations](https://openalex.org/works?filter=cites:W4412887759) openalex · 2026-10-18[Findings of the Association for Computational Linguistics: ACL 2025](https://aclanthology.org/volumes/2025.findings-acl/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2025.findings-acl.1117/)
- [PDF](https://aclanthology.org/2025.findings-acl.1117.pdf)
- [DOI 10.18653/v1/2025.findings-acl.1117](https://doi.org/10.18653/v1/2025.findings-acl.1117)
- [OpenAlex](https://openalex.org/W4412887759)

**Authors (6)**

1. William Sheffield
2. Kanishka Misra
3. [Valentina Pyatkin](https://openalex.org/A5034624275) h 10 · 379 citations · 86 works
4. [Ashwini Deo](https://openalex.org/A5089341224) h 21 · 1386 citations · 74 works
5. [Kyle Mahowald](https://openalex.org/A5039468724) [ORCID](https://orcid.org/0000-0002-9786-8716) · h 26 · 4046 citations · 194 works
6. [Junyi Jessy Li](https://openalex.org/A5133312868) h 1 · 4 citations · 13 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The English discourse particle "just" has several senses: exclusive ("Betsy just eats chicken nuggets"), temporal ("just arrived"), unelaboratory ("Fido is just a dog"), unexplanatory ("the lights just turn on and off") and emphatic ("just delicious"). The authors test whether instruction-tuned LLMs can tell these senses apart. They use 90 hand-built sentences, 15 per sense, each with one clear sense and no context, and 149 sentences from movie subtitles labelled by semanticists. In the first task, a model picks one of six labels; the log-probabilities of the label continuations are compared. Chance is 0.167 on the hand-built data and 0.403 on subtitles, where the exclusive sense is the majority label. Models beat chance on the hand-built set, except Llama-3.2-1b, and the largest, Llama-3.3-70b, is not much better than mid-size models. Accuracy falls by 0.24 on average on subtitles. Adding context changes it by -0.05 on average. In the second task, models judge whether two sentences use "just" in the same way. Only the adjective and temporal senses are reliably separated. On the four target senses, only the unelaboratory and emphatic senses are separated in a few larger models. Ten models were run at temperature 0.

## Ideas

- I1. `prag/src/frames.rs` or a small decision list in `prag`: "just" senses from explicit features: a following verb with a temporal adverb (temporal), "just a" (unelaboratory), a following adjective or intensifier (emphatic), "just" plus a sentence-final "why" reading (unexplanatory). Write it as an ordered rule list, so each label names the rule that fired.
- I2. Build a 90-sentence hand-made set for the Mova school (15 per sense, one sense each, no context), and report accuracy against chance 1/6. A shuffled-label control must score at chance.
- I3. Pairwise same-sense test without a model: represent each sentence by its rule features, then check that same-sense pairs score above different-sense pairs, with a rank test rather than a t-test. This tests the rules' internal consistency.
- I4. Context check: add one preceding sentence and require that accuracy does not drop by more than the paper's 0.05 average. A rule that reads the context window is a deterministic way to test this.

## For Mova

The chapters are [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/) (function words and particles), [30. Pragmatics and discourse: overview](https://md.mova.elamur.ai/free/en/docs/languages/en/30-pragmatics-overview/) and [33. Implicature and presupposition](https://md.mova.elamur.ai/free/en/docs/languages/en/33-implicature-and-presupposition/) (exclusives). The code is `prag/src/frames.rs` for features, `en/src/dict.rs` for the entry for "just", and `world/src/knowledge.rs` for the test items. The measure: accuracy on the 90 hand-built sentences against chance 0.167, run twice, plus the shuffled-label control.

Topics: [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.findings-acl.1117](https://doi.org/10.18653/v1/2025.findings-acl.1117)

```
@inproceedings{sheffield2025is,
  title = {{Is It textit JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs}},
  author = {Sheffield, William and Misra, Kanishka and Pyatkin, Valentina and Deo, Ashwini and Mahowald, Kyle and Li, Junyi Jessy},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  year = {2025},
  doi = {10.18653/v1/2025.findings-acl.1117},
  url = {https://doi.org/10.18653/v1/2025.findings-acl.1117}
}
```

- [← previous Encoding Discourse Structure: Comparison of RST and QUD](https://md.mova.elamur.ai/free/en/docs/papers/shahmohammadi-2023-encoding-discourse-structure-comparison-rst/)
- [next → Pragmatically Informative Text Generation](https://md.mova.elamur.ai/free/en/docs/papers/shen-2019-pragmatically-informative-text-generation/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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