Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.04534 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918046306664448 |
|---|---|
| author | Sheffield, William Misra, Kanishka Pyatkin, Valentina Deo, Ashwini Mahowald, Kyle Li, Junyi Jessy |
| author_facet | Sheffield, William Misra, Kanishka Pyatkin, Valentina Deo, Ashwini Mahowald, Kyle Li, Junyi Jessy |
| contents | Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse effects, as exemplified by the diverse uses of the particle "just" (e.g., exclusive, temporal, emphatic). This work investigates the capacity of LLMs to distinguish the fine-grained senses of English "just", a well-studied example in formal semantics, using data meticulously created and labeled by expert linguists. Our findings reveal that while LLMs exhibit some ability to differentiate between broader categories, they struggle to fully capture more subtle nuances, highlighting a gap in their understanding of discourse particles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04534 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs Sheffield, William Misra, Kanishka Pyatkin, Valentina Deo, Ashwini Mahowald, Kyle Li, Junyi Jessy Computation and Language Artificial Intelligence Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse effects, as exemplified by the diverse uses of the particle "just" (e.g., exclusive, temporal, emphatic). This work investigates the capacity of LLMs to distinguish the fine-grained senses of English "just", a well-studied example in formal semantics, using data meticulously created and labeled by expert linguists. Our findings reveal that while LLMs exhibit some ability to differentiate between broader categories, they struggle to fully capture more subtle nuances, highlighting a gap in their understanding of discourse particles. |
| title | Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.04534 |