GGATN combines graph grounding with transformer self- and cross-attention to generate full event sequences, timestamps, length, and attributes in a single pass followed by Viterbi-style constrained decoding, outperforming prompted LLM baselines on six logs with zero hallucinated activities.
ACM Transactions on Intelligent Systems and Technology (TIST) 10, 1–34
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Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring
GGATN combines graph grounding with transformer self- and cross-attention to generate full event sequences, timestamps, length, and attributes in a single pass followed by Viterbi-style constrained decoding, outperforming prompted LLM baselines on six logs with zero hallucinated activities.