Local citation recommendation (LCR) suggests a set of papers for a citation placeholder within a given context.This paper introduces Cite-BART, citation-specific pre-training within an encoder-decoder architecture, where authordate citation tokens are masked to learn to reconstruct them to fulfill LCR.The global version (CiteBART-Global) extends the local context with the citing paper's title and abstract to enrich the learning signal.CiteBART-Global achieves state-of-the-art performance on LCR benchmarks except for the FullTextPeerRead dataset, which is quite small to see the advantage of generative pre-training.The effect is significant in the larger benchmarks, e.g., Refseer and ArXiv., with the Refseer pre-trained model emerging as the best-performing model.We perform comprehensive experiments, including an ablation study, a qualitative analysis, and a taxonomy of hallucinations with detailed statistics.Our analyses confirm that CiteBART-Global has a cross-dataset generalization capability; the macro hallucination rate (MaHR) at the top-3 predictions is 4%, and when the ground-truth is in the top-k prediction list, the hallucination tendency in the other predictions drops significantly.We publicly share our code 1 , base datasets 2 , global datasets 3 , and pre-trained models 4 to support reproducibility.