Using Chains of Thought to Prompt Machine-Learned Models Pre-Trained on Diversified Objectives
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. The example method includes updating one or more parameters of the machine-learned model based on an evaluation of the plurality of outputs.
1 . A computer-implemented method for improved prompting of a machine-learned model, the method comprising:
obtaining, by a computing system comprising one or more processors, 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;
inputting, by the computing system and to the machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model has been pre-trained using a plurality of diversified objectives; and
generating, by the computing system, using the machine-learned model and responsive to the operative query, an operative response.
2 . The computer-implemented method of claim 1 , wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence to generate an operative trace of intermediate states from the operative query to the operative response.
3 . The computer-implemented method of claim 1 , wherein:
the instructive sequence is prepended to the operative query; and
the instructive trace comprises a chain of intermediate responses to intermediate queries.
4 . The computer-implemented method of claim 1 , wherein the instructive sequence comprises a tokenized representation of a natural language.
5 . The computer-implemented method of claim 1 , wherein generating the operative response comprises:
generating, by the computing system and using the machine-learned model, a plurality of operative responses; and
determining, by the computing system, the operative response based on a sample of the plurality of operative responses.
6 . The computer-implemented method of claim 1 , wherein the operative query is a first query component and the operative response is a first response component, and wherein the method comprises:
inputting, by the computing system and to the machine-learned model, the instructive sequence, the first query component, the first response component, and a second query component; and
generating, by the computing system, using the machine-learned model and responsive to the second query component, a second response component.
7 . The computer-implemented method of claim 1 , wherein to pre-train the machine-learned model using the plurality of diversified objectives the machine-learned model has been pre-trained using a plurality of different combinations of configuration parameters of a pretraining objective framework.
8 . The computer-implemented method of claim 7 , wherein the machine-learned model has been pre-trained on a plurality of corrupted training examples that were generated from one or more training examples, wherein the plurality of corrupted training examples were respectively generated according to the plurality of different combinations of configuration parameters.
9 . The computer-implemented method of claim 8 , wherein the pre-training objectives required to machine-learned model to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples.
10 . The computer-implemented method of claim 7 , wherein the configuration parameters comprise two or more different parameters of: a subportion length parameter, a subportion quantity parameter, or a corruption rate parameter.
11 . The computer-implemented method of claim 7 , wherein the plurality of different combinations of configuration parameters comprise:
a distributed configuration configured for generating a plurality of corrupted subportions distributed over a training example; and
a sequential configuration configured for generating a corrupted subportion corresponding to a terminus of the training example.
12 . The computer-implemented method of claim 7 , wherein the plurality of different combinations of configuration parameters comprise:
a first distributed configuration configured for generating a first plurality of corrupted subportions distributed over a training example;
a second distributed configuration configured for generating a second plurality of corrupted subportions distributed over the training example, wherein the second distributed configuration is configured to cause greater corruption of the training example than the first distributed configuration; and
a sequential configuration configured for generating a corrupted subportion corresponding to a terminus of the training example.
13 . The computer-implemented method of claim 1 , wherein at least one of the plurality of diversified objectives comprises a bidirectional masked language modeling objective.
14 . One or more memory devices storing non-transitory computer-readable instructions for improved prompting of a machine-learned model, the instructions executable to cause one or more processors to perform operations, the operations comprising:
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;
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, and wherein the machine-learned model has been pre-trained using a plurality of diversified objectives; and
generating using the machine-learned model and responsive to the operative query, an operative response.
15 . The one or more memory devices of claim 14 , wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence to generate an operative trace of intermediate states from the operative query to the operative response.
16 . The one or more memory devices of claim 14 , wherein:
the instructive sequence is prepended to the operative query; and
the instructive trace comprises a chain of intermediate responses to intermediate queries.
17 . The one or more memory devices of claim 14 , wherein to pre-train the machine-learned model using the plurality of diversified objectives the machine-learned model has been pre-trained using a plurality of different combinations of configuration parameters of a pretraining objective framework.
18 . The one or more memory devices of claim 17 , wherein the machine-learned model has been pre-trained on a plurality of corrupted training examples that were generated from one or more training examples, wherein the plurality of corrupted training examples were respectively generated according to the plurality of different combinations of configuration parameters.
19 . The one or more memory devices of claim 14 , wherein at least one of the plurality of diversified objectives comprises a bidirectional masked language modeling objective.
20 . A computing system for improved prompting of a machine-learned model, the system comprising:
one or more processors; and
one or more memory devices storing non-transitory computer-readable instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
obtaining a chain of thought prompt comprising an instructive trace through a series of intermediate states;
inputting, to a machine-learned model, the chain of thought prompt, wherein the machine-learned model has been pre-trained using a plurality of diversified objectives; and
generating using the machine-learned model and responsive to the chain of thought prompt, an operative response.