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Our preliminary experiments on the CoDEx dataset demonstrate that GA-S2S outperforms competitive Seq2Seq-based baseline models, achieving up to a 19\\% relative gain in link prediction accuracy.","title":"Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction","url":"https://arxiv.org/abs/2605.18211","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.18211v1 Announce Type: cross \nAbstract: We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve link prediction in knowledge graphs. While existing Seq2Seq models rely solely on surface-level textual descriptions of entities and relations and at best, flatten the neighborhoods of a query entity into a single linear sequence, thereby discarding the inherent graph structure, GA-S2S jointly encodes both textual features and the full $k$-hop subgraph topology surrounding the query entity. 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