Apart from the empirical work in Tilburg and Antwerp, a number of recent studies on statistical natural language processing (e.g. Dagan & Lee, 1997; <cite>Collins & Brooks, 1995)</cite> also suggest that, contrary to common wisdom, forgetting specific training items, even when they represent extremely low-frequency events, is harmful to generalization accuracy. After reviewing this empirical work briefly, I will report on new results (work in progress in collaboration with van den Bosch and Zavrel), systematically comparing greedy and lazy learning techniques on a number of benehrnark natural language processing tasks: tagging, grapheme-to-phoneme conversion, and pp-attachment.