Enhancing Trustworthiness of Large Language Models through Dynamic Knowledge Graph Reasoning

Despite significant advances in large language models, many reasoning datasets are still built from a fixed set of predefined relations, manually curated types such as cause, effect, and intent found in knowledge graph datasets such as ATOMIC and COMET. While these predefined relations provide essential structure, the fixed schema limits relational coverage and adaptability to novel contexts. We present DYNA-SKILL, a dual-triple knowledge graph framework that preserves 35 predefined relations consolidated and refined from existing commonsense knowledge graph datasets while augmenting them with 133 additional schema-free dynamic relations generated via a self-prompting mechanism. Each instance consists of two linked triples (Head–Predefined Relation–Tail) and (Tail–Dynamic Relation–Additional Tail) used as independent training samples while retaining linkages for extended reasoning paths. Across reasoning-intensive benchmarks, including CommonsenseQA, RiddleSense, and ARC Challenge, the Hybrid configuration, which combines predefined and dynamically generated relations, achieves performance comparable to or slightly higher than Predefined-only settings and yields up to 3.2\% higher accuracy than baseline BERT models. By expanding the relation set from 35 predefined types to a total of 168 relations, DYNA-SKILL enriches relational diversity and improves multi-step logical reasoning, which can enhance performance in real-world scenarios such as complex question answering, multi-document analysis, and causal reasoning, where accurate and adaptable inference is critical.