AnyEdit++ proposes Bayes-Chunk, an adaptive segmentation method based on Bayesian Surprise, with theoretical claims of structural independence and causal locality, reporting superior results over baselines on math, code, and narrative tasks.
arXiv preprint arXiv:2312.05497 , year=
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Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.
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AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise
AnyEdit++ proposes Bayes-Chunk, an adaptive segmentation method based on Bayesian Surprise, with theoretical claims of structural independence and causal locality, reporting superior results over baselines on math, code, and narrative tasks.