Transformer-based Language Models for Reasoning in the Description Logic ALCQ

• URL: http://arxiv.org/abs/2410.09613v1
• Date: Sat, 12 Oct 2024 18:25:34 GMT
• Title: Transformer-based Language Models for Reasoning in the Description Logic ALCQ
• Authors: Angelos Poulis, Eleni Tsalapati, Manolis Koubarakis,
• Abstract summary: We construct the natural language dataset, DELTA$_D$, using the expressive description logic language $mathcalALCQ$. We investigate the logical reasoning capabilities of a supervised fine-tuned DeBERTa-based model and two large language models. We show that the DeBERTa-based model fine-tuned on our dataset can master the entailment checking task.
• Score: 2.8210912543324658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in transformer-based language models have sparked research into their logical reasoning capabilities. Most of the benchmarks used to evaluate these models are simple: generated from short (fragments of) first-order logic sentences with only a few logical operators and quantifiers. We construct the natural language dataset, DELTA$_D$, using the expressive description logic language $\mathcal{ALCQ}$. DELTA$_D$ comprises 384K examples and increases in two dimensions: i) reasoning depth, and ii) linguistic complexity. In this way, we systematically investigate the logical reasoning capabilities of a supervised fine-tuned DeBERTa-based model and two large language models (GPT-3.5, GPT-4) with few-shot prompting. We show that the DeBERTa-based model fine-tuned on our dataset can master the entailment checking task. Moreover, 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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