Abstract: Provided are systems and methods for automated language testing for use in continuous integration and continuous delivery software development. In one example, the method may include identifying language tests in a plurality of different natural languages to be performed for a software application, executing the language tests and validating the language tests based on results of executed scripts, automatically assigning the language tests among a plurality of language testers based on execution of a machine learning model which pairs together a language test with a language tester based on test attributes and tester attributes, and transmitting notifications to computing devices of the plurality of language testers with information about the automatically assigned language tests.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An example process includes receiving an utterance including a user request, determining, based on the user request, a domain associated with the user request, determining, based on the domain, a first subsequent user action and a second subsequent user action, determining, based on the domain, a first parameter for the first subsequent user action and a second parameter for the second subsequent user action, in accordance with a determination that a first score associated with the first subsequent user action is higher than a score associated with the second subsequent user action, selecting the first subsequent user action as a suggested subsequent user action, and providing the suggested subsequent user action.

Abstract: A system for answering queries using one or more families of large language models (h-LLMs) including a processor, a communication device, and storage with software that, when executed by the processor, is operable to receive a user prompt, generate derived prompts from the user prompt, generate prompt embeddings from the derived prompts, transmit the plurality of prompt embeddings to a vector database, the vector database comprising a database of knowledge documents, each knowledge document comprised by the database of knowledge documents having one or more embeddings associated therewith, receive knowledge documents relevant to the prompt embeddings, generate context-aware prompts, transmit the context-aware prompts to the one or more h-LLMs, receive h-LLM results, process the h-LLM results to produce processed h-LLM results, each having a score, identify one or more preferred results responsive to the scores, and transmit the one or more preferred results to a user via the user interface.

Abstract: Implementations relate to managing multimedia content that is obtained by large language model(s) (LLM(s)) and/or generated by other generative model(s). Processor(s) of a system can: receive natural language (NL) based input that requests multimedia content, generate a response that is responsive to the NL based input, and cause the response to be rendered. In some implementations, and in generating the response, the processor(s) can process, using a LLM, LLM input to generate LLM output, and determine, based on the LLM output, at least multimedia content to be included in the response. Further, the processor(s) can evaluate the multimedia content to determine whether it should be included in the response. In response to determining that the multimedia content should not be included in the response, the processor(s) can cause the response, including alternative multimedia content or other textual content, to be rendered.

Abstract: An electronic device for providing a corrected response message according to an utterance intention of a user by using a keyword included in an input inquiry input by the user, and an operation method of the electronic device are provided. The electronic device includes receiving the input inquiry input by the user, identifying a representative inquiry according to an utterance intention of the user by analyzing the input inquiry by using a natural language understanding (NLU) model, extracting a keyword from the input inquiry by comparing a vector value of a first embedding vector of the input inquiry changed through the NLU model with a vector value of a second embedding vector of the representative inquiry, and correcting a response message mapped to correspond to the representative inquiry, by using the extracted keyword.

Abstract: A method for processing text data includes analyzing the text data to identify a plurality of keywords. The method also includes determining whether each of the plurality of keywords already exists in one or more databases. When a keyword in the plurality of keywords is not found in the one or more databases, the method includes tagging the keyword with a plurality of characters for storage. The plurality of characters includes at least a first character to indicate a start of the tagging, a second character to indicate a corresponding database for storing the keyword, and a third character to indicate an end of the tagging. The method also includes storing the tagged keyword in the corresponding database.

Abstract: Data is received that includes a passage of text generated in response to a prompt which comprises a plurality of sentences. Thereafter, the passage of text is tokenized into a plurality of tokens each corresponding to a different word in the passage of text. A first classification head of an adaptive fine-tuned transforms classifies each of the tokens into one of a plurality of classes. A second classification head of the adaptive fine-tuned transformer model classifies each of the sentences as either including or not including an argument. Data can then be provided which characterizes the first and second classifications. Related apparatus, systems, techniques and articles are also described.

Abstract: The present document relates to a method of layered encoding of a compressed sound representation of a sound or sound field. The compressed sound representation comprises a basic compressed sound representation comprising a plurality of components, basic side information for decoding the basic compressed sound representation to a basic reconstructed sound representation of the sound or sound field, and enhancement side information including parameters for improving the basic reconstructed sound representation.

Abstract: Examples provide enhanced machine learning model accuracy through post-hoc confidence score calibration. A machine learning (ML) system receives results generated by an ML model, the results comprising at least one confidence score and electronic documents. The ML system processes the results generated by the ML model comprising performing document understanding by extracting data points from the electronic documents. The ML system associates the confidence score with the extracted data points and calibrates a confidence score associated with the extracted data points using a post-hoc calibration solution set. The ML system implements confidence scoring recalibration comprising aligning the confidence score with prediction accuracy and adjusting the generated confidence score by the recalibration. Based on adjusting the confidence score, the ML system extracts an individual element of information from the electronic documents comprising an extracted value.

Abstract: A question answering system includes a dangerous operation determination unit configured to determine whether question-answer pair data includes a dangerous operation, and an answer sentence replacement unit configured to replace, when it is determined that the dangerous operation is included, an answer sentence including a description of the dangerous operation included in a document by a replaced answer sentence depending on a classification of the dangerous operation, to thereby create replaced question-answer pair data.

Abstract: Systems, methods and non-transitory computer readable media for generating content using a generative model without relying on selected training examples are provided. An input indicative of a desire to generate a new content using a generative model may be received. The generative model may be a result of training a machine learning model using a plurality of training examples. Each training example of the plurality of training examples may be associated with a respective content. Further, an indication of a particular subgroup of at least one but not all of the plurality of training examples may be obtained. Based on the indication, the input and the generative model may be used to generate the new content, abstaining from basing the generation of the new content on any training example included in the particular subgroup. The new content may be provided.

Abstract: In some embodiments, a method is provided for updating an editing parameter for a model for automatically suggesting revisions to text data. The method may include displaying, on a graphical user interface (GUI) of a user device, one or more interactive input elements, wherein each of the one or more input elements is associated with an editing parameter for a model for automatically suggesting revisions to text data. The method may include receiving, via the GUI, an input from a selected input element of the one or more input elements, wherein the input comprises an indication of a value for a selected editing parameter associated with the selected input element. The method may include updating the selected editing parameter for the model based on the value. The method may include using the model with the updated selected editing parameter to apply an edit operation to an obtained text-under-analysis.

Abstract: A document is obtained that include natural language text and a set of conditions are extracted from the document. In at least one embodiment, the set of conditions is extracted using a machine learning model trained on other natural language text. A validation process is performed to result in a validated set of conditions and a machine-readable data structure is generated to indicate one or more conditions of the validated set of conditions expressed within the document.

Abstract: Methods, systems, and computer-readable media for the generation of real-time recommendations using natural language processing. The method receives a request for a benefit recommendation; generates at least one tag based on input data; extracts, based on the at least one tag, at least one observation and at least one action from the input data; predicts at least one recommendation based on the extracted at least one observation and the extracted at least one action in real time; and sends the at least one predicted recommendation for display to a user device.

Abstract: The various implementations described herein include methods and devices for speaker diarization. In one aspect, a method includes obtaining an audio recording and generating an embedding signal from the audio recording. The method further includes factoring the embedding signal to obtain a basis matrix and an activation matrix, including obtaining a sparse optimization of the embedding signal by minimizing a norm corresponding to the factored embedding signal. The method also includes generating a speaker log for the audio recording based on the sparse optimization of the embedding signal.

Abstract: A computer-implemented method includes receiving a sequence of acoustic frames as input to an automatic speech recognition (ASR) model. Here, the ASR model includes a causal encoder and a decoder. The method also includes generating, by the causal encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by the decoder, a first probability distribution over possible speech recognition hypotheses. Here, the causal encoder includes a stack of causal encoder layers each including a Recurrent Neural Network (RNN) Attention-Performer module that applies linear attention.

Abstract: A speech recognition system comprises: an input, for receiving an input signal from at least one microphone; a first buffer, for storing the input signal; a noise reduction block, for receiving the input signal and generating a noise reduced input signal; a speech recognition engine, for receiving either the input signal output from the first buffer or the noise reduced input signal from the noise reduction block; and a selection circuit for directing either the input signal output from the first buffer or the noise reduced input signal from the noise reduction block to the speech recognition engine.

Abstract: Methods and systems are provided for customizing an action. In some implementations, voice input is received from a user and a context is determined from the voice input. Potential contextual data is identified based on the context and the voice input. A level of confidence is determined for an association of the potential contextual data and the context. An action is performed based on the voice input, the potential contextual data, and the level of confidence. The potential contextual data is used to customize the action.

Abstract: An apparatus including circuitry configured to obtain a defocus direction; process a spatial audio signal that represents an audio scene to generate a processed spatial audio signal that represents a modified audio scene based on the defocus direction, so as to control relative deemphasis in, at least in part, a portion of the spatial audio signal in the defocus direction relative to at least in part other portions of the spatial audio signal; and output the processed spatial audio signal, wherein the modified audio scene based on the defocus direction enables the deemphasis in, at least in part, the portion of the spatial audio signal in the defocus direction relative to at least in part other portions of the spatial audio signal.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automated prototyping of a topic model. An example method includes a data manipulation engine ingesting and pre-processing source data from a set of data sources, a feature extraction engine that thereafter transforms the pre-processed data into a set of numeric representations of the pre-processed data, and an autonomous model generator that automatically generates a trained topic model using the set of numeric representations. Embodiments further enable visualization of topic model output, which permits a user to easily consume and utilize information from a topic model for any number of purposes.