Transformers in the Service of Description Logic-based Contexts

• URL: http://arxiv.org/abs/2311.08941v3
• Date: Fri, 26 Apr 2024 16:32:02 GMT
• Title: Transformers in the Service of Description Logic-based Contexts
• Authors: Angelos Poulis, Eleni Tsalapati, Manolis Koubarakis,
• Abstract summary: We construct the natural language dataset, DELTA$_D$, using the description logic language $mathcalALCQ$. We investigate the reasoning ability of a supervised fine-tuned DeBERTa-based model and of two large language models (GPT-3.5, GPT-4) with few-shot prompting. Our results demonstrate that the DeBERTa-based model can master the reasoning task and that the performance of GPTs can improve significantly even when a small number of samples is provided.
• Score: 2.8210912543324658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in transformer-based models have initiated research interests in investigating their ability to learn to perform reasoning tasks. However, most of the contexts used for this purpose are in practice very simple: generated from short (fragments of) first-order logic sentences with only a few logical operators and quantifiers. In this work, we construct the natural language dataset, DELTA$_D$, using the description logic language $\mathcal{ALCQ}$. DELTA$_D$ contains 384K examples, and increases in two dimensions: i) reasoning depth, and ii) linguistic complexity. In this way, we systematically investigate the reasoning ability of a supervised fine-tuned DeBERTa-based model and of two large language models (GPT-3.5, GPT-4) with few-shot prompting. Our results demonstrate that the DeBERTa-based model can master the reasoning task and that the performance of GPTs can improve significantly even when a small number of samples is provided (9 shots). We open-source our code and datasets.

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