TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
<|reference_start|>TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks: In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet on the example of the LLaMA-2-7b model and test it on multiple lexical semantic tasks. As the outcome of our experiments, we present TaxoLLaMA, the everything-in-one model, lightweight due to 4-bit quantization and LoRA. It achieves 11 SotA results, 4 top-2 results out of 16 tasks for the Taxonomy Enrichment, Hypernym Discovery, Taxonomy Construction, and Lexical Entailment tasks. Moreover, it demonstrates very strong zero-shot performance on Lexical Entailment and Taxonomy Construction with no fine-tuning. We also explore its hidden multilingual and domain adaptation capabilities with a little tuning or few-shot learning. All datasets, code, and model are available online at https://github.com/VityaVitalich/TaxoLLaMA<|reference_end|>
@article{moskvoretskii2024taxollama:,
    title={TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic
  Tasks},
    author={Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, Alexander
  Panchenko, Irina Nikishina},
    journal={https://aclanthology.org/2024.acl-long.127},
    year={2024},
    doi={10.18653/v1/2024.acl-long.127},
    archivePrefix={arXiv},
    eprint={2403.09207},
    primaryClass={cs.CL}
}