Patent ID: 12197317

Embodiments of the disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures, wherein showings therein are for purposes of illustrating embodiments of the disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION

As used herein, the term “network” may comprise any hardware or software-based framework that includes any artificial intelligence network or system, neural network or system and/or any training or learning models implemented thereon or therewith.

As used herein, the term “module” may comprise hardware or software-based framework that performs one or more functions. In some embodiments, the module may be implemented on one or more neural networks.

Evaluation of deep learning models is an essential step to verify how well the trained model can handle unseen queries. Typically, people use some percentage of the original dataset as a test set to validate the trained model. However, this evaluation procedure wouldn't be an optimal estimate. For example, during intent classification task, making the model learn the query “I would like to change password” as “login issues” intent, and testing it on queries like “change password” doesn't guarantee model robustness.

As such, an improved sophisticated test/evaluation set to get a better estimate of model robustness is needed. For example, a testing/evaluation set with sentences like “unable to login”, “forgot username” is used to verify if the model properly understood “login issues” intent. Manual curation of such evaluation sets often is time consuming, especially for chatbots with millions of conversations. Furthermore, when the training dataset is given by administrators, it may vary a lot from real-time customer queries. As such, there is a distribution shift between real-time production queries and training samples. Accordingly, it is desirable to test the model on real data, however, the real data is challenging for manual curation.

The automated testing pipeline model helps to better test the performance of the trained neural network model. By building a diverse evaluation dataset that is different from the training dataset, the evaluation is closer to the real-time model performance.

As described in detail below, an automated testing pipeline model is described to create a testing dataset for validating model robustness. The automated testing pipeline includes a hierarchical combination of a dependency parser, a pretrained language model, and a bag of words (BoW) classifier to curate easy and hard evaluation datasets from real time customer data. For example, “hard eval sets” may include the samples that are diverse enough from the training data (e.g., provided by the administer) used to training the neural network model for evaluating.

While the description below uses intent classification tasks as an example, the automated testing pipeline model may apply to various tasks beyond intent classification tasks, including e.g., dialogue act detection, sentiment classification, topic classification etc. Furthermore, the automated testing pipeline model is language-agnostic. Given components in the automated testing pipeline model work in multilingual settings, it can apply to hundreds of languages.

Moreover, the automated testing pipeline model helps to improve the model performance by establishing a feedback cycle between model and human (referred to as human-in-the-loop), where human/manual adjustment to the training set is performed based on the evaluation result. For example, the initial training dataset may be provided by administrators based on their prior, and does not reflect the actual interacted utterances with chat-bots implemented using the neural network model. The automated testing pipeline model may identify gaps, and communicate them to the administrators, and the administrators may adjust the training dataset based on the feedback from the automated testing pipeline model, e.g., by adding missed/harder samples to the training set. As such, human-in-the-loop feedback cycles may lead to better model performance after each interaction of the feedback cycle.

FIG.1is a simplified diagram illustrating a computing device implementing the Automated Testing Pipeline framework described throughout the specification, according to one embodiment described herein. As shown inFIG.1, computing device100includes a processor110coupled to memory120. Operation of computing device100is controlled by processor110. And although computing device100is shown with only one processor110, it is understood that processor110may be representative of one or more central processing units, multi-core processors, microprocessors, microcontrollers, digital signal processors, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs) and/or the like in computing device100. Computing device100may be implemented as a stand-alone subsystem, as a board added to a computing device, and/or as a virtual machine.

Memory120may be used to store software executed by computing device100and/or one or more data structures used during operation of computing device100. Memory120may include one or more types of machine-readable media. Some common forms of machine-readable media may include floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any other medium from which a processor or computer is adapted to read.

Processor110and/or memory120may be arranged in any suitable physical arrangement. In some embodiments, processor110and/or memory120may be implemented on a same board, in a same package (e.g., system-in-package), on a same chip (e.g., system-on-chip), and/or the like. In some embodiments, processor110and/or memory120may include distributed, virtualized, and/or containerized computing resources. Consistent with such embodiments, processor110and/or memory120may be located in one or more data centers and/or cloud computing facilities.

In some examples, memory120may include non-transitory, tangible, machine readable media that includes executable code that when run by one or more processors (e.g., processor110) may cause the one or more processors to perform the methods described in further detail herein. For example, as shown, memory120includes instructions for Automated Testing Pipeline module130that may be used to implement and/or emulate the systems and models, and/or to implement any of the methods described further herein. An Automated Testing Pipeline module130may receive input140such as user queries data via the data interface115and generate an output150, which may be a testing dataset generated based on the user queries data.

The data interface115may comprise a communication interface, a user interface (such as a voice input interface, a graphical user interface, and/or the like). For example, the computing device100may receive the input140(such as a training dataset) from a networked database via a communication interface. Or the computing device100may receive the input140, such as an articulated question, from a user via the user interface.

In some embodiments, the Automated Testing Pipeline module130is configured to generate an answer in response to an image and a question based on the image. The Automated Testing Pipeline module130may further include a dependency parser submodule131, a pretrained language model submodule132, a bag of words classifier submodule133, which are all further described below. In one embodiment, the Automated Testing Pipeline module130and its submodules131-134may be implemented by hardware, software and/or a combination thereof.

In one embodiment, the Automated Testing Pipeline module130and its submodules131-133, may be implemented by hardware, software and/or a combination thereof.

In one embodiment, the Automated Testing Pipeline module130and one or more of its submodules131-133may be implemented using one or more artificial neural network. The neural network comprises a computing system that is built on a collection of connected units or nodes, referred as neurons. Each neuron receives an input signal and then generates an output by a non-linear transformation of the input signal. Neurons are often connected by edges, and an adjustable weight is often associated to the edge. The neurons are often aggregated into layers such that different layers may perform different transformations on the respective input and output transformed input data onto the next layer. Therefore, the neural network may be stored at memory120as a structure of layers of neurons, and parameters describing the non-linear transformation at each neuron and the weights associated with edges connecting the neurons. An example neural network may be a pretrained language model, and/or the like.

In one embodiment, the neural network based automated testing pipeline module130and one or more of its submodules131-133may be trained by updating the underlying parameters of the neural network based on a loss, e.g., a metric that evaluates how far away a neural network model generates a predicted output value from its target output value (also referred to as the “ground-truth” value). Given the computed loss, the negative gradient of the loss function is computed with respect to each weight of each layer individually. Such negative gradient is computed one layer at a time, iteratively backward from the last layer to the input layer of the neural network. Parameters of the neural network are updated backwardly from the last layer to the input layer (backpropagating) based on the computed negative gradient to minimize the loss. The backpropagation from the last layer to the input layer may be conducted for a number of training samples in a number of training epochs. In this way, parameters of the neural network may be updated in a direction to result in a lesser or minimized loss, indicating the neural network has been trained to generate a predicted output value closer to the target output value.

Some examples of computing devices, such as computing device100may include non-transitory, tangible, machine readable media that include executable code that when run by one or more processors (e.g., processor110) may cause the one or more processors to perform the processes of method. Some common forms of machine-readable media that may include the processes of method are, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, and/or any other medium from which a processor or computer is adapted to read.

FIG.2is a simplified block diagram of a networked system suitable for implementing the Automated Testing Pipeline framework in embodiments described herein. In one embodiment, block diagram200shows a system including the user device210which may be operated by user240, data vendor servers245,270and280, server230, and other forms of devices, servers, and/or software components that operate to perform various methodologies in accordance with the described embodiments. Exemplary devices and servers may include device, stand-alone, and enterprise-class servers which may be similar to the computing device100described inFIG.1, operating an OS such as a MICROSOFT® OS, a UNIX® OS, a LINUX® OS, or other suitable device and/or server-based OS. It can be appreciated that the devices and/or servers illustrated inFIG.2may be deployed in other ways and that the operations performed, and/or the services provided by such devices and/or servers may be combined or separated for a given embodiment and may be performed by a greater number or fewer number of devices and/or servers. One or more devices and/or servers may be operated and/or maintained by the same or different entities.

The user device210, data vendor servers245,270and280, and the server230may communicate with each other over a network260. User device210may be utilized by a user240(e.g., a driver, a system admin, etc.) to access the various features available for user device210, which may include processes and/or applications associated with the server230to receive an output data anomaly report.

User device210, data vendor server245, and the server230may each include one or more processors, memories, and other appropriate components for executing instructions such as program code and/or data stored on one or more computer readable mediums to implement the various applications, data, and steps described herein. For example, such instructions may be stored in one or more computer readable media such as memories or data storage devices internal and/or external to various components of system200, and/or accessible over network260.

User device210may be implemented as a communication device that may utilize appropriate hardware and software configured for wired and/or wireless communication with data vendor server245and/or the server230. For example, in one embodiment, user device210may be implemented as an autonomous driving vehicle, a personal computer (PC), a smart phone, laptop/tablet computer, wristwatch with appropriate computer hardware resources, eyeglasses with appropriate computer hardware (e.g., GOOGLE GLASS®), other type of wearable computing device, implantable communication devices, and/or other types of computing devices capable of transmitting and/or receiving data, such as an IPAD® from APPLE®. Although only one communication device is shown, a plurality of communication devices may function similarly.

User device210ofFIG.2contains a user interface (UI) application212, and/or other applications216, which may correspond to executable processes, procedures, and/or applications with associated hardware. For example, the user device210may receive a message indicating an answer to a visual question from the server230and display the message via the UI application212. In other embodiments, user device210may include additional or different modules having specialized hardware and/or software as required.

In various embodiments, user device210includes other applications216as may be desired in particular embodiments to provide features to user device210. For example, other applications216may include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate application programming interfaces (APIs) over network260, or other types of applications. Other applications216may also include communication applications, such as email, texting, voice, social networking, and IM applications that allow a user to send and receive emails, calls, texts, and other notifications through network260. For example, the other application216may be an email or instant messaging application that receives a prediction result message from the server230. Other applications216may include device interfaces and other display modules that may receive input and/or output information. For example, other applications216may contain software programs for asset management, executable by a processor, including a graphical user interface (GUI) configured to provide an interface to the user240to view the answer.

User device210may further include database218stored in a transitory and/or non-transitory memory of user device210, which may store various applications and data and be utilized during execution of various modules of user device210. Database218may store user profile relating to the user240, predictions previously viewed or saved by the user240, historical data received from the server230, and/or the like. In some embodiments, database218may be local to user device210. However, in other embodiments, database218may be external to user device210and accessible by user device210, including cloud storage systems and/or databases that are accessible over network260.

User device210includes at least one network interface component219adapted to communicate with data vendor server245and/or the server230. In various embodiments, network interface component219may include a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency, infrared, Bluetooth, and near field communication devices.

Data vendor server245may correspond to a server that hosts one or more of the databases203a-n(or collectively referred to as203) to provide training datasets including training images and questions to the server230. The database203may be implemented by one or more relational database, distributed databases, cloud databases, and/or the like.

The data vendor server245includes at least one network interface component226adapted to communicate with user device210and/or the server230. In various embodiments, network interface component226may include a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency, infrared, Bluetooth, and near field communication devices. For example, in one implementation, the data vendor server245may send asset information from the database203, via the network interface226, to the server230.

The server230may be housed with the Automated Testing Pipeline module130and its submodules described inFIG.1. In some implementations, module130may receive data from database219at the data vendor server245via the network260to generate an answer to a visual question. The generated answer may also be sent to the user device210for review by the user240via the network260.

The database232may be stored in a transitory and/or non-transitory memory of the server230. In one implementation, the database232may store data obtained from the data vendor server245. In one implementation, the database232may store parameters of the Automated Testing Pipeline module130. In one implementation, the database232may store previously generated answers, and the corresponding input feature vectors.

In some embodiments, database232may be local to the server230. However, in other embodiments, database232may be external to the server230and accessible by the server230, including cloud storage systems and/or databases that are accessible over network260.

The server230includes at least one network interface component233adapted to communicate with user device210and/or data vendor servers245,270or280over network260. In various embodiments, network interface component233may comprise a DSL (e.g., Digital Subscriber Line) modem, a PSTN (Public Switched Telephone Network) modem, an Ethernet device, a broadband device, a satellite device and/or various other types of wired and/or wireless network communication devices including microwave, radio frequency (RF), and infrared (IR) communication devices.

Network260may be implemented as a single network or a combination of multiple networks. For example, in various embodiments, network260may include the Internet or one or more intranets, landline networks, wireless networks, and/or other appropriate types of networks. Thus, network260may correspond to small scale communication networks, such as a private or local area network, or a larger scale network, such as a wide area network or the Internet, accessible by the various components of system200.

FIG.3is a simplified block diagram illustrating an automated testing pipeline framework300used for providing a testing dataset and an improved training dataset for a trained neural network model, according to one embodiment described herein. As shown inFIG.3, the performance of a neural network model304trained using the training dataset302is to be evaluated. The real-time user queries from the user-bot chats306, which is provided by the trained neural network model304, are collected.

The automated testing pipeline model308receives the user queries from the user-bot chats306, and generates testing datasets320. Specifically, the automated testing pipeline model308includes a hierarchical combination of a dependency parser308, a pretrained language model310, and a naive bag of words classifier312.

Referring toFIGS.3and4, in some embodiments, the dependency parser308of the automated testing pipeline model308may filter user queries without action verbs and/or action-object pairs.FIG.4illustrates some example samples eliminated by the dependency parser308and some example output samples of the dependency parser308. For example, samples without action verbs402(e.g., “Yes,” “Something else,” “Ok, thanks. That's a good start!,” etc.) are eliminated by the dependency parser308. For further example, output samples404of the dependency parser308may include samples with action verbs (e.g., “delete my account,” “do you have a picture of grains?” “what size do i need to select?,” etc.). Various types of dependency parsers (e.g., an open-source SpaCy dependency parser, etc.) may be used to implement the dependency parser308of the automated testing pipeline model308.

Referring toFIGS.3and5, in some embodiments, a pretrained language model310receives the filtered output samples404from the dependency parser308. The pretrained language model310may label and rank the received user queries, e.g., by doing semantic search with the training dataset302(including e.g., admin provided intent set samples314for training the model304to perform an intent classification task). Various types of pretrained language models (e.g., Microsoft's sentence-transformers/all-mpnet-base-v2, etc.) may be used to implement the pretrained language model310. In some embodiments, the pretrained language model may be selected based on its performance on semantic similarity tasks.

Referring toFIG.5, illustrated therein are example inputs and outputs of the pretrained language model310. For example, the input502includes intent dataset samples defined by the administrator (e.g., in a training dataset provided by the administrator), including the intent of “intent_loginIssues” with associated queries, “I need to reset my password” and “I cannot reset my pas . . . .” The output504includes user queries together with corresponding classification labels and confidence scores (used for rankings) provided by the pretrained language model. For example, output504includes an example classification of “intent_loginIssues” for the user query “I need to reset my password,” a similarity confidence (ranking) score of 87%.

In some embodiments, to minimize the bias caused by an individual pretrained language model in semantic searches, an ensemble approach for ranking samples by using multiple pretrained language models may be used. For example, the combined ranking of rankings from the multiple pretrained language models may be used. In some embodiments, the combination may be based on weights assigned to the multiple pretrained language models respectively, where a weight may be assigned based on the bias evaluation of a particular pretrained language model. In other embodiments, a manual review step may be performed to reduce such bias.

Referring toFIGS.3and6, in some embodiments, a bag of words classifier312is used to detect samples that are very similar to the administrator defined intent sets, e.g., based on exact keyword matches. Various types of bag of words classifiers may be used to implement the bag of words classifier132, including e.g., term frequency-inverse document frequency (TF-IDF) vectorizer, Linear Support Vector Machine (SVM), any other suitable bag of words classifier, and/or a combination thereof.

Referring toFIG.6, example inputs and outputs of the bag of words classifier312are illustrated. For example, the example input602includes the administrator defined intent set, including e.g., “intent_loginIssues” associated with queries “I need to reset my password” and “I cannot reset my pas . . . ”. The example output605of the bag of words classifier314includes prediction based on exact keyword matches with the administrator defined intent set. For example, for query “I need to reset my password,” the bag of words classifier314correctly predicts its intention to be “intent_loginIssues” based on the input602based on exact keyword match. As such, the query “I need to reset my password” may be considered as an easy example, and using this query may not provide a good estimate of the robustness of the trained neural network model. For further example, the bag of words classifier314mis-predict the intent for the query “Need help to login” based on exact key word match, and as such, may be considered as a hard example, which is diverse enough from the admin provided training data. Using such a hard example for testing the trained neural network model may provide a better estimate of how well the application (e.g., a chat robot) using the trained neural network does on real time customer/user queries.

Referring toFIGS.3and7, in some embodiments, a filter316of the automated testing pipeline model308may be used to generate filtered user queries based on the results from the pretrained language model310and the bag of words classifier312. For example, the filter316may remove easy samples as identified by the BoW classifier312. For further example, the filter316may remove samples that have the same prediction from the BoW classifier312and the PLM310, which are easy samples for testing. In some embodiments, the filter316generates N filtered user queries by taking the top N ranked user queries from the remaining user queries. The ranking may be determined based on the diversity with the administrator intent set (e.g., a user query with a lower similarity confidence score is ranked higher).

Referring toFIG.7, illustrated are examples of eliminated samples702by the filter316. For example, the eliminated samples702may include easy samples (e.g., “I need to reset my password,” “I can't reset my password”) that have exact keyword matches with the administrator intent set or have the same predictions by the BoW classifier312and the PLM310. The eliminated samples702may include samples (e.g., “cannot recover password”) with a lower ranking, which indicates less diversity with the administrator intent set (e.g., a user query with a higher similarity confidence score) provided by the PLM310.

The exampleFIG.7also illustrates examples of output samples704of the filter316. For example, after filtering is performed to eliminate samples702, the filter316provides output samples704including samples having better diversity with the administrator intent set, including e.g., “Need help to login,” “I have a new email address,” “Need to find my user ID,” “can I change my username?,” and “I am unable to log on. The system does not send a message.”

Referring back toFIG.3, in some embodiments, the output of the filter316is used as the testing dataset320. In other embodiments, optionally, the automated testing pipeline framework300may receive manual selection/labeling inputs318, which is used to select the testing dataset320from the output of the filter316of the automated testing pipeline model308.

The testing dataset320may be sent to the trained model304that is to be tested, which generates testing results322that may be used to evaluate the performance of the trained model304. Training dataset updates324may be generated based on the testing results322.

In some embodiments, the training dataset updates324are generated automatically, e.g., by adding samples in the testing dataset320with poor performance results. In some other embodiments, a feedback cycle between model and human (referred to as human-in-the-loop) is established, and human/manual adjustment to the training set is performed based on the testing results. The automated testing pipeline model may identify gaps, and communicate them to the administrators, and the administrators may adjust the training dataset based on the feedback from the automated testing pipeline model, e.g., by adding missed/harder samples to the training set. As such, human-in-the-loop feedback cycles may lead to better model performance after each interaction of the feedback cycle.

The training dataset updates324may be sent to update the training dataset302, which may be used to train the neural network model304. Multiple training and testing cycles may be performed to improve the model performance based on the testing results in the previous cycle.

FIG.8is an example logic flow diagram illustrating a method of using the automated testing pipeline model to provide a testing dataset to test the performance of a neural network model and to the performance of the neural network by updating the training set using test results, according to some embodiments described herein. The method800may begin at block802, where the neural network model (e.g., used to implement a chat-bot) is trained using a first training dataset (e.g., provided by an administer).

At block804, an initial testing dataset for the trained neural network is received, where the initial testing dataset includes a first plurality of user queries. In some embodiments, to better test the model's robustness, it is desirable to use real-time data (e.g., customer queries in chat-bots applications). That's because in embodiments where the model is trained on the administrator provided dataset, there may exist a distribution shift between real-time production queries and training samples. The initial testing dataset may include a large amount of user queries (e.g., millions of chat conversations). While it is challenging to manually filter such a large amount of user queries to create a better testing dataset (e.g., including harder samples), as discussed below, the automated testing pipeline model filters the initial testing dataset automatically to generate the testing dataset.

At block806, a dependency parser of the automated testing pipeline model generates a second plurality of user queries by filtering the first plurality of user queries based on one or more action verbs. As such, each user query of the second plurality of user queries has one or more action verbs.

At block808, a pretrained language model receives the second plurality of user queries, and generates classification/prediction (e.g., the intent for performing an intent classification test) for the user queries. The pretrained language model may also generate a corresponding similarity confidence score for each of the second plurality of user queries. The confidence score may be used to rank the queries based on respective relationships with queries in the first training dataset. In various embodiments, the pretrained language model may be optionally finetuned by training using the first training dataset (e.g., the administrator provided intent dataset).

At block810, a bag of words classifier may be used to generate classification/prediction (e.g., the intent for performing an intent classification test) for the user queries based on exact keyword matches with the first training dataset. The bag of words classifier may be finetuned by training using the first training dataset (e.g., the administrator provided intent dataset).

At block812, a filter may be used to update the second plurality of user queries based on the results from the pretrained language model and the bag of words classifier. For example, the filter may be used to eliminate user queries that have the same predictions from the pretrained language model and the bag of words classifier. For further example, the filter may be used to keep only the top N user queries from the remaining user queries based on the ranking of the user queries based on its relation to the first training dataset. For example, the user queries that are more diverse from the first training dataset (e.g., harder samples) are ranked higher for selection.

At block814, optionally, manual selection and/or labeling by an operator may be performed to update the second plurality of user queries (e.g., to reduce the bias of the pretrained language model, etc.).

At block816, a testing dataset is provided using the updated second plurality of user queries. The testing dataset is sent to the trained neural network model to be tested. The trained neural network model generates test outputs using the testing dataset.

At block818, a second training set may be generated based on the testing output. For example, training dataset updates may be generated based on the testing results, which may be used to update the first training set to generate the second training set. In some embodiments, the training dataset updates are generated automatically, e.g., by adding samples in the testing dataset with poor performance results to the first training dataset. In some other embodiments, a feedback cycle between model and human (referred to as human-in-the-loop) is established, and human/manual adjustment to the training set is performed based on the testing results. The automated testing pipeline model may identify gaps, and communicate them to the administrators, and the administrators may adjust the training dataset based on the feedback from the automated testing pipeline model, e.g., by adding missed/harder samples to the training set. As such, human-in-the-loop feedback cycles may lead to better model performance after each interaction of the feedback cycle.

At block820, the second training dataset may be used to train the neural network model. Multiple training and testing cycles as described in method800may be performed to improve the model performance based on the testing results in the previous cycle.

FIGS.9A-9Bprovide example experimental results illustrating example data performance of the automated testing pipeline model described in relation toFIGS.1-8, according to some embodiments described herein. As shown inFIGS.9A and9B, —distributed stochastic neighbor embedding (TSNE) method is used to plot the two-dimensional visualization graphs. The TSNE method is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.

FIGS.9A and9Bare TSNE plots illustrating sentence embedding projection of administrator defined samples, and the testing samples generated using the automated testing pipeline model using Gettys data. As shown inFIGS.9A and9B, for both intents (“loginIssues intent” and “creditsRemaining intent”), the testing dataset obtained with the automated testing pipeline model diverges enough from the original ADMIN provided samples. Using a diverse evaluation set, as provided by the automated testing pipeline can help to assess the generalization power of a model, because it allows the model to be tested on a variety of data that is representative of the real-world data it will encounter in practice. When a model is trained on a diverse training set and then evaluated on a diverse evaluation set, it can be more confidently assumed that the model will perform well on real-world data because it has been tested on a variety of data that is similar to what it will encounter in practice. Using a diverse evaluation set can also help to identify any biases or weaknesses in the model. For example, if the model performs poorly on certain types of data or data from certain demographics, this can indicate that the model may not be fully generalizable and may need further development or fine-tuning. Overall, using a diverse evaluation set can be an important step in ensuring that a model is well-suited for real-world applications and can help to identify any areas for improvement.

The automated testing pipeline model may be implemented for various needs. In some embodiments, the automated testing pipeline model may be used to detect out-of-distribution (OOD) automatically, based on the rankings of the samples. In some embodiments, the automated testing pipeline model may be implemented on a small group of user data (e.g., on few-hundred chats) to understand how frequent are hard eval examples.

This description and the accompanying drawings that illustrate inventive aspects, embodiments, implementations, or applications should not be taken as limiting. Various mechanical, compositional, structural, electrical, and operational changes may be made without departing from the spirit and scope of this description and the claims. In some instances, well-known circuits, structures, or techniques have not been shown or described in detail in order not to obscure the embodiments of this disclosure. Like numbers in two or more figures represent the same or similar elements.

In this description, specific details are set forth describing some embodiments consistent with the present disclosure. Numerous specific details are set forth in order to provide a thorough understanding of the embodiments. It will be apparent, however, to one skilled in the art that some embodiments may be practiced without some or all of these specific details. The specific embodiments disclosed herein are meant to be illustrative but not limiting. One skilled in the art may realize other elements that, although not specifically described here, are within the scope and the spirit of this disclosure. In addition, to avoid unnecessary repetition, one or more features shown and described in association with one embodiment may be incorporated into other embodiments unless specifically described otherwise or if the one or more features would make an embodiment non-functional.

Although illustrative embodiments have been shown and described, a wide range of modification, change and substitution is contemplated in the foregoing disclosure and in some instances, some features of the embodiments may be employed without a corresponding use of other features. One of ordinary skill in the art would recognize many variations, alternatives, and modifications. Thus, the scope of the invention should be limited only by the following claims, and it is appropriate that the claims be construed broadly and, in a manner, consistent with the scope of the embodiments disclosed herein.