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

翻译：本文以LLaMA-2-7b模型为例，探索大语言模型从WordNet中获取词汇语义知识的能力，并在多个词汇语义任务上对其进行测试。基于实验结果，我们提出了TaxoLLaMA——一个集多种功能于一体的轻量化模型，该模型通过4位量化和LoRA技术实现高效部署。在分类体系扩展、上位词发现、分类体系构建及词汇蕴含等16项任务中，该模型取得了11项最先进性能指标，并在4项任务中位列前二。此外，该模型在词汇蕴含和分类体系构建任务上展现出无需微调的强大零样本性能。我们还通过少量微调或少样本学习，探索了其潜在的多语言适应与领域适应能力。所有数据集、代码及模型均已发布于https://github.com/VityaVitalich/TaxoLLaMA。