An extension of the Data2Text framework is presented that introduces a collaborative neural-symbolic approach to explainable data-to-text generation that reduces manual rule engineering effort, increases linguistic variability, and supports adaptation to new domains and languages, while maintaining full explainability.
Answer Set Programming (ASP) and Large Language Models (LLMs) offer complementary strengths in symbolic reasoning and natural language understanding. In this work, we present an integrated framework for reliable structured knowledge extraction from natural language that combines the syntactic capabilities of LLMs with...
Mario Alviano, Matteo Capalbo, G. Gottlob et al.· Journal of Artificial Intell...· 0 citations
SLITE is presented, an explainable hybrid model for Recognizing Textual Entailment that integrates two complementary layers of semantic analysis: a structural-relational layer, based on semantic compatibility and incompatibility between compositional entities, and a distributional-informational layer, based on structur...
David Torres-Moreno, J. Hermosillo-Valadez, Asela Reig-Alamillo· 0 citations
A layer-wise analysis indicates that surface-level features such as temporality and negation are captured more reliably than deeper semantic phenomena like quantification in large language models, highlighting the limited capacity of current LLMs to generate fully formal meaning representations.
Rémi De Vergnette, Maxime Amblard· International Conference on...· 0 citations
Logical reasoning with large language models (LLMs) is a critical capability, as it reflects a system's ability to correctly deduce hypotheses from a given context using faithful deductive processes. However, LLM reasoning has often been shown to be sensitive to small surface-level variations in problem formulation, ra...
Ramya Keerthy Thatikonda, W. Buntine, Ehsan Shareghi· 0 citations
Modern software systems are increasingly variant-rich, and feature models capture this variability formally. The Universal Variability Language (UVL) has emerged as a community-driven standard for expressing such models, but creating and maintaining UVL artifacts remains a manual, expert-driven task. Large Language Mod...
Johannes Stümpfle, Fethiye Tunç, N. Jazdi et al.· Proceedings of the ACM/IEEE...· 0 citations
This paper introduces a fully graph-based pipeline where the classifier never directly processes the input text, and reveals that graphs and text are complementary: combining both modalities achieves 92.1% accuracy on SNLI.
Younes Boufouss, Luc Pommeret, Thomas Gerald et al.· 0 citations
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