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arXiv · 2510.24179

Exploring the Influence of Relevant Knowledge for Natural Language Generation Interpretability

Iván Martínez-Murillo·Paloma Moreda·Elena Lloret
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Abstract

This paper explores the influence of external knowledge integration in Natural Language Generation (NLG), focusing on a commonsense generation task. We extend the CommonGen dataset by creating KITGI, a benchmark that pairs input concept sets with retrieved semantic relations from ConceptNet and includes manually annotated outputs. Using the T5-Large model, we compare sentence generation under two conditions: with full external knowledge and with filtered knowledge where highly relevant relations were deliberately removed. Our interpretability benchmark follows a three-stage method: (1) identifying and removing key knowledge, (2) regenerating sentences, and (3) manually assessing outputs for commonsense plausibility and concept coverage. Results show that sentences generated with full knowledge achieved 91\% correctness across both criteria, while filtering reduced performance drastically to 6\%. These findings demonstrate that relevant external knowledge is critical for maintaining both coherence and concept coverage in NLG. This work highlights the importance of designing interpretable, knowledge-enhanced NLG systems and calls for evaluation frameworks that capture the underlying reasoning beyond surface-level metrics.

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Iván Martínez-Murillo, Paloma Moreda, Elena Lloret. 2025-10-28. Exploring the Influence of Relevant Knowledge for Natural Language Generation Interpretability. https://doi.org/10.5121/csit.2025.152002

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