TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

• URL: http://arxiv.org/abs/2403.09207v2
• Date: Mon, 17 Jun 2024 16:43:10 GMT
• Title: TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks
• Authors: Viktor Moskvoretskii, Ekaterina Neminova, Alina Lobanova, Alexander Panchenko, Irina Nikishina,
• Abstract summary: In this paper, we explore the capabilities of LLMs in capturing lexical-semantic knowledge from WordNet. We present TaxoLLaMA, the everything-in-one model, lightweight due to 4-bit quantization and LoRA.
• Score: 54.01153045234468
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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

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