Reparameterized clustering algorithm to harvest in-context examples for nl2sql

A system comprising:

• one or more processors;
• and
• one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising:
• access production logs comprising natural language utterances and corresponding queries in a programming query language;
• embedding, by an embedding model, the natural language utterances and corresponding queries in a programming query language into vectors;
• reducing, using a dimension reduction algorithm, dimension of the vectors to obtain vector representations;
• reparametrizing, using the vector representations, a clustering algorithm for identification of paraphrases of the examples and the examples having unique queries, based on:
• sampling the vector representations to obtain a set of sampled vector representations;
• estimating parameter values for a local radius parameter from a distribution of pairwise distances determined between each of the sampled vector representation in the set of vector representations and each vector representation in the vector representations, and
• iteratively executing the clustering algorithm on the set of sampled vector representations, based on incrementally relaxing density constraints from high-density to low-density clusters based on the estimated parameter values for the local radius parameter and parameter values for a minimum samples parameter, to generate a reparametrized clustering algorithm;
• performing hyperparameter tuning on the reparametrized clustering algorithm by iteratively adjusting, based on clustering evaluation metrics, hyperparameters of the reparametrized clustering algorithm to generate a finetuned reparametrized clustering algorithm;
• augmenting the examples with ground truth examples to generate augmented examples;
• executing the fine-tuned reparametrized clustering algorithm using the augmented examples to generate clustered data points, wherein the clustered data points represent groups of the paraphrases of the examples and the examples having the unique queries;
• and
• fine-tuning or training a generative model using at least the paraphrases of the examples and the examples having the unique queries.

One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• reparametrizing, using vector representations of examples, a clustering algorithm for identification of paraphrases of the examples and the examples having unique queries, based on:
• sampling the vector representations to obtain a set of sampled vector representations;
• estimating parameter values for an initial value, a final value, and an incremental value of a local radius parameter from a distribution of pairwise distances determined between each of the sampled vector representation in the set of vector representations and each vector representation in the vector representations,
• determining parameter values for a smallest minimum sample value, a largest minimum sample value, and an incremental value of a minimum samples parameter, and
• iteratively executing the clustering algorithm on the set of sampled vector representations, based on incrementally relaxing density constraints from high-density to low-density clusters based on the estimated parameter values for the local radius parameter and the determined parameter values for the minimum samples parameter, to generate a reparametrized clustering algorithm;
• performing hyperparameter tuning on the reparametrized clustering algorithm by iteratively adjusting, based on clustering evaluation metrics, hyperparameters of the reparametrized clustering algorithm to generate a finetuned reparametrized clustering algorithm;
• augmenting the examples with ground truth examples to generate augmented examples;
• executing the fine-tuned reparametrized clustering algorithm using the augmented examples to generate clustered data points, wherein the clustered data points represent groups of the paraphrases of the examples and the examples having the unique queries;
• and
• fine-tuning or training a generative model using at least the paraphrases of the examples and the examples having the unique queries.