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Recent developments in Transformer language models now allow users to predict the probability of different sentences and to predict missing words more accurately than before. This new information and perspective can be used to form judgments on novel textual emendations and to further quantify existing historical editorial judgments. We examine the importance of analyzing an author’s corpus, and the impact of the Good-Turing theory of frequency estimation when predicting missing words. We will also outline some of the limits of what Transformer language models can do, and how to practically evaluate them.

Instructions for self-treatment, whether printed, presented via computer or by audiovisual means, are effective in the management of phobias, panic disorder, other anxieties, depression, bulimia nervosa, obesity, alcohol problems, nicotine abuse, myocardial infarction, AIDS, compliance problems and the counseling of patients' relatives. A lasting improvement has been shown for up to 7 years. The mechanisms of effective self-change are discussed.