Abstract: Systems and methods for generative language model tuning can include training the generative language model to generate sets of output text tokens with set of intermediary text tokens with training examples that include input and output pairs. The training can include processing the input with the language model to determine a predicted output and a predicted set of intermediary text tokens. The predicted set of intermediary text tokens can then be evaluated based at least in part on the output associated with the input and the predicted output.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating learned representations of digital circuit designs. One of the systems includes obtaining data representing a program that implements a digital circuit design, the program comprising a plurality of statements; processing the obtained data to generate data representing a graph representing the digital circuit design, the graph comprising: a plurality of nodes representing respective statements of the program, a plurality of first edges each representing a control flow between a pair of statements of the program, and a plurality of second edges each representing a data flow between a pair of statements of the program; and generating a learned representation of the digital circuit design, comprising processing the data representing the graph using a graph neural network to generate a respective learned representation of each statement represented by a node of the graph.

Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example method can include inputting, to a machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative response.

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples.

Abstract: Generally, the present disclosure is directed to the generation and use of property signatures for computer programs. In particular, property signatures can serve as a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type ?in and output type ?out, a property can be a function of type: (?in, ?out)?Bool that (e.g., informally) describes some simple property of the function under consideration. For instance, if ?in and ?out are both lists of the same type, one property might ask ‘is the input list the same length as the output list?’.