Abstract: Implementations described herein relate to attribution of a natural language (NL) based summary generated using a large language model (LLM). Processor(s) of a system can: receive NL based input associated with a client device, generate the NL based summary using the LLM, and process the NL based summary to determine whether a NL based summary segment of the NL based summary matches a dataset segment of a dataset that was utilized to initially train the LLM and/or to fine-tune the LLM. Further, the processor(s) can, in response to determining that the NL based summary segment matches the dataset segment, modify the NL based summary segment of the NL based summary to generate a modified NL based summary. Moreover, the processor(s) can cause the modified NL based summary to be rendered at the client device. The attribution of the NL based summary can be provided as a service to various third-parties.

Abstract: Techniques are described herein for iterative code generation using neural language models. In various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. The first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. Based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. The masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. The second translation may include infill(s) of corrected source code in place of one or more of the masks.

Abstract: Implementations are described herein for predicting symbolic transformation templates to automate source code transformations. In various implementations, pair(s) of predecessor and successor source code snippets may be processed using a symbolic transformation template prediction (STTP) model to predict a symbolic transformation template that includes a predecessor portion that matches the predecessor source code snippet(s) of the pair(s) and a successor portion that matches the successor source code snippet(s) of the pair(s). At least one additional predecessor source code snippet may be identified that matches the predecessor portion of the predicted symbolic transformation template. Placeholders of the predecessor portion of the predicted symbolic transformation template may be bound to one or more tokens of the at least one additional predecessor source code snippet to create binding(s).

Abstract: Implementations described herein relate to attribution of a natural language (NL) based summary generated using a large language model (LLM). Processor(s) of a system can: receive NL based input associated with a client device, generate the NL based summary using the LLM, and process the NL based summary to determine whether a NL based summary segment of the NL based summary matches a dataset segment of a dataset that was utilized to initially train the LLM and/or to fine-tune the LLM. Further, the processor(s) can, in response to determining that the NL based summary segment matches the dataset segment, modify the NL based summary segment of the NL based summary to generate a modified NL based summary. Moreover, the processor(s) can cause the modified NL based summary to be rendered at the client device. The attribution of the NL based summary can be provided as a service to various third-parties.

Abstract: Techniques are described herein for iterative code generation using neural language models. In various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. The first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. Based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. The masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. The second translation may include infill(s) of corrected source code in place of one or more of the masks.

Abstract: Techniques are described herein for iterative code generation using neural language models. In various implementations, an original source code snippet in a first programming language may be processed using a translation machine learning model to generate a first translation of the original source code snippet in a second programming language. The first translation of the original source code snippet may be evaluated to identify error(s) in the first translation. Based on the error(s), respective mask(s) may be inserted to generate a masked first translation of the original source code snippet in the second programming language. The masked first translation of the original source code snippet may be processed using the translation machine learning model to generate a second translation of the original source code snippet in the second language. The second translation may include infill(s) of corrected source code in place of one or more of the masks.

Abstract: Implementations are described herein for predicting symbolic transformation templates to automate source code transformations. In various implementations, pair(s) of predecessor and successor source code snippets may be processed using a symbolic transformation template prediction (STTP) model to predict a symbolic transformation template that includes a predecessor portion that matches the predecessor source code snippet(s) of the pair(s) and a successor portion that matches the successor source code snippet(s) of the pair(s). At least one additional predecessor source code snippet may be identified that matches the predecessor portion of the predicted symbolic transformation template. Placeholders of the predecessor portion of the predicted symbolic transformation template may be bound to one or more tokens of the at least one additional predecessor source code snippet to create binding(s).