Transformers in the Service of Description Logic-Based Contexts
Angelos
Poulis, Eleni
Tsalapati, and Manolis
Koubarakis
In Knowledge Engineering and Knowledge Management, 2025
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 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.
