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CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples
Kyohoon Jin, Juhwan Choi, Jungmin Yun, Junho Lee, Soojin Jang, Youngbin Kim
https://arxiv.org/abs/2508.21083

CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples
Deep learning models often learn and exploit spurious correlations in training data, using these non-target features to inform their predictions. Such reliance leads to performance degradation and poor generalization on unseen data. To address these limitations, we introduce a more general form of counterfactual data augmentation, termed counterbias data augmentation, which simultaneously tackles multiple biases (e.g., gender bias, simplicity bias) and enhances out-of-distribution robustness. W…

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
https://arxiv.org/abs/2508.20736

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Many works at the intersection of Differential Privacy (DP) in Natural Language Processing aim to protect privacy by transforming texts under DP guarantees. This can be performed in a variety of ways, from word perturbations to full document rewriting, and most often under local DP. Here, an input text must be made indistinguishable from any other potential text, within some bound governed by the privacy parameter $\varepsilon$. Such a guarantee is quite demanding, and recent works show that pr…

SPARQL in N3: SPARQL CONSTRUCT as a rule language for the Semantic Web (Extended Version)
D\"orthe Arndt, William Van Woensel, Dominik Tomaszuk
https://arxiv.org/abs/2508.13041

SPARQL in N3: SPARQL CONSTRUCT as a rule language for the Semantic Web (Extended Version)
Reasoning in the Semantic Web (SW) commonly uses Description Logics (DL) via OWL2 DL ontologies, or SWRL for variables and Horn clauses. The Rule Interchange Format (RIF) offers more expressive rules but is defined outside RDF and rarely adopted. For querying, SPARQL is a well-established standard operating directly on RDF triples. We leverage SPARQL CONSTRUCT queries as logic rules, enabling (1) an expressive, familiar SW rule language, and (2) general recursion, where queries can act on the r…

Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
Swayamjit Saha
https://arxiv.org/abs/2507.04625 https://

Knowledge-Aware Self-Correction in Language Models via Structured Memory Graphs
Large Language Models (LLMs) are powerful yet prone to generating factual errors, commonly referred to as hallucinations. We present a lightweight, interpretable framework for knowledge-aware self-correction of LLM outputs using structured memory graphs based on RDF triples. Without retraining or fine-tuning, our method post-processes model outputs and corrects factual inconsistencies via external semantic memory. We demonstrate the approach using DistilGPT-2 and show promising results on simpl…