Abstract: Generation of candidate words for detection of text data generated by an artificial intelligence (AI) model includes obtaining a plurality of character codes associated with a plurality of characters. The plurality of characters is associated with a plurality of words. Based on the plurality of character codes, a first set of candidate words is generated. Each candidate word of the first set of candidate words comprises a combination of at least two character codes of the plurality of character codes. Further, based on an application of a set of predefined criteria on the first set of candidate words, a second set of candidate words is generated. The set of predefined criteria is associated with a likelihood of generation of each of the first set of candidate words by the AI model. The second set of candidate words is output for detecting the text data generated by the AI model.

Abstract: A computer-implemented method receives a first table and a second table into a table evaluation system. Each of a plurality of cells of the first table and the second table are converted into a two-dimensional point with a cell weight related to the size of the cell. An edit distance matrix is computed between the cells of the first table and the cells of the second table, where the edit distance matrix being obtained is based on contents of cells. An optimal transport distance matrix is calculated between the cells of the first table and the cells of the second table by using the two-dimensional point and the cell weight for each of the plurality of cells. An output is generated that indicates a correspondence between cells in the first table and cells in the second table by using the edit distance matrix and the optimal transport distance matrix.

Abstract: A syntactic pre-training task is selected from among a set of syntactic pre-training tasks. For the selected syntactic pre-training task, the pre-trained language model is retrained by using an optimization function which prevents catastrophic forgetting during the retraining. Inferencing is performed using the retrained language model.