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# In the LLM era, Word Sense Induction remains unsolved

**Authors:** Anna Mosolova, [Marie Candito](https://md.mova.elamur.ai/free/en/docs/papers/authors/marie-candito/), [Carlos Ramisch](https://md.mova.elamur.ai/free/en/docs/papers/authors/carlos-ramisch/) · **Year:** 2025 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2025 **Link:** [aclanthology.org/2025.findings-acl.882](https://aclanthology.org/2025.findings-acl.882/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**1** citations](https://openalex.org/works?filter=cites:W4412888176) openalex · 2026-10-18[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lex-2025-mosolova-wsi-unsolved/) of the 140673 papers whose bibliographies we readFindings of ACL 2025 (Vienna)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://doi.org/10.18653/v1/2025.findings-acl.882)
- [PDF](https://aclanthology.org/2025.findings-acl.882.pdf)
- [DOI 10.18653/v1/2025.findings-acl.882](https://doi.org/10.18653/v1/2025.findings-acl.882)
- [arXiv 2603.11686](https://arxiv.org/abs/2603.11686)
- [OpenAlex](https://openalex.org/W4412888176)

**Authors (3)**

1. [Anna Mosolova](https://openalex.org/A5079756261) [ORCID](https://orcid.org/0009-0006-3442-1577) · h 2 · 25 citations · 14 works
2. [Marie Candito](https://openalex.org/A5056181885) [ORCID](https://orcid.org/0000-0001-8306-4859) · h 21 · 1397 citations · 149 works
3. [Carlos Ramisch](https://openalex.org/A5024831674) [ORCID](https://orcid.org/0000-0001-7466-9039) · h 20 · 1569 citations · 201 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 paper argues that word sense induction (WSI), which groups the occurrences of a word into senses without a sense list, is badly evaluated. The usual SemEval sets over-represent polysemous words and do not keep the natural polysemy and frequency of a corpus. The authors build a SemCor-derived set that keeps them, and compare pre-trained embeddings and clustering methods across parts of speech, with an LLM-based method. The strong baseline is "one cluster per lemma" (1cpl), which scores 73.6 on the F-B3 measure, and no unsupervised method beats it. The best previous system, PolyLM, scores 73.0, and adding enough unlabelled examples to the clustering is reported to beat it. The authors conclude that WSI is not solved, and that LLMs struggle with the task.

## Ideas

- I1. `onto/src/wsd.rs`: report the one-cluster-per-lemma baseline next to every WSI or WSD score. Any new Mova method must beat it on the same SemCor-derived set. The negative control is the baseline itself, which should reproduce its own score exactly.
- I2. Deterministic clustering of contexts: for each lemma in the school silver corpus, represent each occurrence by its head-dependent pairs from the `en` parse, and cluster with a fixed threshold in a BTreeMap-ordered loop. Measure F-B3 against a held-out annotated set, with the 1cpl baseline beside it.
- I3. Instances per lemma: measure the F-B3 of the clustering as the number of corpus instances per lemma grows (for example 10, 50, 100), to find the point where it passes 1cpl. The paper's threshold of 50 is the first value to test.

## For Mova

The code is `onto/src/wsd.rs` for the baseline and `en/src/parse.rs` for the context features, with `en ctx-report` as a check. The chapter is [19. The lexicon: vocabulary, multiword expressions and register](https://md.mova.elamur.ai/free/en/docs/languages/en/19-lexicon-mwe-register/). The paper's data are SemCor-derived, so use them for tests and measurement only. The LLM parts are not for Mova: the large model may only label training data offline. Measure first: F-B3 for the 1cpl baseline and for the clustering, run twice and compared.

Topics: [Word sense disambiguation](https://md.mova.elamur.ai/free/en/docs/papers/topics/word-sense-disambiguation/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2025.findings-acl.882](https://doi.org/10.18653/v1/2025.findings-acl.882) · [arXiv 2603.11686](https://arxiv.org/abs/2603.11686)

```
@inproceedings{mosolova2025llm,
  title = {{In the LLM era, Word Sense Induction remains unsolved}},
  author = {Mosolova, Anna and Candito, Marie and Ramisch, Carlos},
  booktitle = {Findings of ACL 2025 (Vienna)},
  year = {2025},
  doi = {10.18653/v1/2025.findings-acl.882},
  eprint = {2603.11686},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.findings-acl.882}
}
```

- [← previous Do Large Language Models Understand Word Senses?](https://md.mova.elamur.ai/free/en/docs/papers/lex-2025-meconi-llms-word-senses/)
- [next → Open English NameNet: Extending English Wordnet with Names](https://md.mova.elamur.ai/free/en/docs/papers/lex-2026-mccrae-open-english-namenet/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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