---
title: "Are Large Language Models Good at Lexical Semantics? A Case"
description: "The authors build a training resource from the noun subgraph of English WordNet 3.0. 39 references, cited by 12 papers here."
authors: ["Viktor Moskvoretskii", "Alexander Panchenko", "Irina Nikishina"]
year: 2024
venue: "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)"
source: "https://aclanthology.org/2024.lrec-main.133/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/moskvoretskii-2024-are-large-language-models-good/"
---
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# Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning

**Authors:** [Viktor Moskvoretskii](https://md.research.elamur.ai/free/en/authors/viktor-moskvoretskii/), [Alexander Panchenko](https://md.research.elamur.ai/free/en/authors/alexander-panchenko/) and [Irina Nikishina](https://md.research.elamur.ai/free/en/authors/irina-nikishina/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2024](https://md.research.elamur.ai/free/en/venues/lrec/2024/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.lrec-main.133/) · [PDF](https://aclanthology.org/2024.lrec-main.133.pdf) · [OpenAlex](https://openalex.org/W7131777315)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [12](https://md.research.elamur.ai/free/en/papers/moskvoretskii-2024-are-large-language-models-good/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether instruction-tuned open LLMs can handle lexical semantics, using taxonomy learning (IS-A relations between concepts) as a test. Earlier Transformer-based attempts gave weak results, and recent models had not been tested. The authors build a training resource from the noun subgraph of English WordNet 3.0. A stochastic traversal of the IS-A DAG yields four kinds of subtasks: hyponym prediction, hypernym prediction, synset mixing (one hyponym shared by two synsets) and insertion (a node between a given hypernym and hyponym). Sampling probabilities control how many examples of each kind are drawn, and the procedure ensures that no test node appears in training. They fine-tune Llama2-7B and Mistral-7B with LoRA and compare three input encodings: sense numbers, lemmas and definitions. Templates that include definitions work best. The tuned models then reach state-of-the-art results on TexEval-2 (SemEval-2016 Task 13). This shows that relatively small open LLMs can be adapted for taxonomy construction and enrichment. Data, code and models are publicly released.

## Key points
- **Dataset:** 20,236 training and 1,274 test samples drawn from WordNet nouns. Subtask sampling probabilities were 0.51 for hyponym, 0.39 for hypernym, and 0.05 each for synset mixing and insertion. Each sample went to the test set with probability 0.05, and nodes were restricted to topological level ≥ 3.
- **Fine-tuning setup:** Llama2-7B and Mistral-7B, 4-bit quantization, LoRA, one epoch, batch size 64, AdamW with learning rate 3e-4 and cosine schedule. GPT2 scored near zero (mean MRR 0.021) and was dropped.
- **Test-set MRR:** Llama2-7B with definitions had the best mean (0.247), ahead of lemmas (0.188) and numbers (0.162). Mistral-7B with definitions was strongest on hypernym prediction (0.498).
- **Downstream results:** state-of-the-art on the TexEval-2 Environment dataset and near-SotA on the Science dataset.
- **Release:** resources are available at github.com/uhh-lt/lexical_llm.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Viktor Moskvoretskii, Alexander Panchenko and Irina Nikishina. 2024. Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{moskvoretskii2024are,
  title = {{Are Large Language Models Good at Lexical Semantics? A Case of Taxonomy Learning}},
  author = {Viktor Moskvoretskii, Alexander Panchenko and Irina Nikishina},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/4k6x3aybmcru},
  url = {https://doi.org/10.63317/4k6x3aybmcru}
}
```

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## References

39 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 3 November 2026*