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，探究大语言模型从WordNet中捕获词汇语义知识的能力，并在多种词汇语义任务上进行测试。实验结果表明，我们提出的TaxoLLaMA集成模型采用4位量化与LoRA技术实现轻量化，在分类体系增强、上位词发现、分类体系构建及词汇蕴涵等16项任务中，取得11项最优结果及4项次优结果。该模型无需微调即可在词汇蕴涵与分类体系构建任务中展现出极强的零样本性能。此外，通过少量微调或小样本学习，我们进一步揭示了其隐藏的多语言与领域自适应能力。所有数据集、代码及模型均已开源：https://github.com/VityaVitalich/TaxoLLaMA