ABSTRACT
Abstract
Methods and systems for automatically prompting a LLM to generate a personalized text, such as a personalized textual description, in which portions of the text are customized based on user attributes. In various examples, responsive to a request for a textual description, a user record is retrieved for a user associated with the request and one or more user attributes are obtained based on the user record. In examples, a prompt to a large language model (LLM) for generating a user-specific textual description is generated, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text. The prompt is provided to the LLM to receive a generated user-specific textual description. The generated user-specific textual description is provided for display via a user device.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
The present disclosure claims priority from U.S. provisional patent No. 63/482,403, filed Jan. 31, 2023, and U.S. provisional patent No. 63/483,727, filed Feb. 7, 2023, the entireties of which are hereby incorporated by reference.
FIELD
The present disclosure relates to machine learning, and, more particularly, to generation of prompts to large language models (LLM), and, yet more particularly, to prompting an LLM to generate personalized textual descriptions.
BACKGROUND
A large language model (LLM) is a deep learning algorithm that can process natural language to summarize, translate, predict and generate text and other content. A LLM may be trained to learn billions of parameters in order to model how words relate to each other in a textual sequence. Inputs to a LLM may be referred to as prompts. A prompt is a natural language input that includes instructions to cause the LLM to generate a desired output.
SUMMARY
Conventionally, to generate a textual description of an object (e.g., a product, a location, an image, a video, a piece of music, a tangible thing, etc.), a user may prompt a LLM to generate an object description. The prompt to the LLM may be simply a list of the object attributes including the object name and optionally other attributes. The prompt may then be inputted into the LLM and the generated text may be directly outputted to the user from the LLM. However, the generated text from the LLM may be generic and static and unlikely to be 100% relevant to all users.
In various examples, the present disclosure provides a technical solution that automatically generates a prompt to cause the LLM to generate a personalized textual description using information about a user or information about a class of users as input to the LLM. In this way, text that could appeal to specific users can be automatically included in the textual description.
In various examples, the present disclosure describes systems and methods that enable a LLM to be prompted to generate a personalized textual description using user attributes (e.g., related to an individual user's preferences and needs) as inputs. Examples of the present disclosure may enable the generated personalized textual description to be automatically or semi-automatically generated, which may provide for greater efficiency and/or reduced need for user inputs. The disclosed solution may improve the performance of e-commerce platforms or merchant websites by presenting textual descriptions to specific users in a manner that is more relevant and appealing to the user.
In some examples, the present disclosure describes a computing system including: a processing unit configured to execute computer-readable instructions to cause the system to: responsive to a request for a textual description, retrieve a user record for a user associated with the request; obtain one or more user attributes based on the user record; generate a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; provide the prompt to the LLM and receive a generated user-specific textual description; and provide the generated user-specific textual description for display via a user device.
In the previous example aspect of the computing system, wherein the processing unit is configured to execute computer-readable instructions to obtain the one or more user attributes based on the user record by: extracting, by a pre-trained attribute extraction model, the one or more user attributes from the user record.
In the previous example aspect of the computing system, wherein the processing unit is configured to execute computer-readable instructions to further cause the system to: retrieve an object record for an object associated with the request; obtain one or more object attributes based on the object record; append the one or more user attributes to the one or more object attributes; and generate the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description appended to the one or more object attributes to include in the generated user-specific textual description.
In the previous example aspect of the computing system, wherein the processing unit is configured to execute computer-readable instructions to obtain the one or more object attributes by: extracting, by a pre-trained attribute extraction model, the one or more object attributes that are relevant to the user attributes, from the object record.
In the previous example aspect of the computing system, wherein in extracting the one or more object attributes and the one or more user attributes, the processing unit is configured to execute computer-readable instructions to further cause the system to: determine, by the pre-trained attribute extraction model, one or more priority object attributes from the extracted object attributes; determine, by the pre-trained attribute extraction model, one or more priority user attributes from the extracted user attributes; append the one or more priority user attributes to the one or more priority object attributes; and generate the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more priority user attributes to include in the generated user-specific textual description appended to the one or more priority object attributes to include in the generated user-specific textual description.
In some example aspects of the computing system, wherein the one or more user attributes is an embedding.
In some example aspects of the computing system, wherein the one or more object attributes is an embedding.
In some example aspects of the computing system, wherein the request is a request received from a user device to view a webpage associated with an object, and wherein the processing unit is configured to execute computer-readable instructions to further cause the system to: in response to the request, provide a modified webpage for display via the user device, the modified webpage including the user-specific textual description.
In the previous example aspect of the computing system, wherein data for the webpage stored on the system includes the source text, and wherein the modified webpage provided to the user device has the user-specific textual description substituted in real-time in response to the request.
In some example aspects of the computing system, wherein the source text is a source product description for a product associated with the request, the prompt to the LLM includes instructions to generate a user-specific product description for the product, and the generated user-specific textual description is a generated user-specific product description.
In some example aspects of the computing system, wherein the user record comprises at least one of: a current browsing activity record; a previous transaction event record; a previous browsing activity record; a previous search query; or a user profile.
In some example aspects of the computing system, wherein the user attributes include at least one of: a user demographic attribute; a user preference attribute; or a user need attribute.
In some example aspects of the computing system, wherein the processing unit is configured to execute computer-readable instructions to further cause the system to provide the prompt to the LLM as a set of tokens.
In some example aspects of the computing system, wherein the LLM is a generative pre-trained transformer LLM.
In some examples, the present disclosure describes a computer-implemented method for automatically prompting a LLM to generate a personalized text, the method includes a number of steps. The method comprising: responsive to a request for a textual description, retrieving a user record for a user associated with the request; obtaining one or more user attributes based on the user record; generating a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; providing the prompt to the LLM and receive a generated user-specific textual description; and providing the generated user-specific textual description for display via a user device.
In the previous example aspect of the method, wherein obtaining the one or more user attributes based on the user record comprises: extracting, by a pre-trained attribute extraction model, the one or more user attributes from the user record.
In the previous example aspect of the method, further comprising: prior to retrieving the user record, retrieving an object record for an object associated with the request; obtaining one or more object attributes based on the object record; and generating the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description appended to the one or more object attributes to include in the generated user-specific textual description.
In the previous example aspect of the method, wherein obtaining the one or more object attributes based on the object record comprises: extracting, by a pre-trained attribute extraction model, the one or more object attributes that are relevant to the user attributes, from the object record.
In the previous example aspect of the method, wherein extracting the one or more object attributes and the one or more user attributes comprises: determining, by the pre-trained attribute extraction model, one or more priority object attributes from the extracted object attributes; determining, by the pre-trained attribute extraction model, one or more priority user attributes from the extracted user attributes; and generating the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more priority user attributes to include in the generated user-specific textual description appended to the one or more priority object attributes to include in the generated user-specific textual description.
In an example aspect of the method, wherein the one or more user attributes is an embedding.
In an example aspect of the method, wherein the one or more object attributes is an embedding.
In an example aspect of the method, wherein the request is a request received from a user device to view a webpage associated with a product, and wherein the method further comprises: in response to the request, providing a modified webpage for display on the user device, the modified webpage including the user-specific textual description.
In the previous example aspect of the method, wherein data for the webpage stored on the system includes the source text, and wherein the modified webpage provided to the user device has the user-specific textual description substituted in real-time in response to the request.
In an example aspect of the method, wherein the source text is a source product description, the prompt to the LLM includes instructions to generate a user-specific product description for a product associated with the source product description, and the generated user-specific textual description is a generated user-specific product description.
In an example aspect of the method, wherein the user record comprises at least one of: a current browsing activity record; a previous transaction event record; a previous browsing activity record; a previous search query; or a user profile.
In an example aspect of the method, wherein the user attributes include at least one of: a user demographic attribute; a user preference attribute; or a user need attribute.
In an example aspect of the method, further comprising: providing the prompt to the LLM as a set of tokens.
In an example aspect of the method, wherein the LLM is a generative pre-trained transformer LLM.
In some examples, the present disclosure describes a computer-readable medium storing instructions that, when executed by a processor of a computing system, cause the computing system to: responsive to a request for a textual description, retrieve a user record for a user associated with the request; obtain one or more user attributes based on the user record; generate a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; provide the prompt to the LLM and receive a generated user-specific textual description; and provide the generated user-specific textual description for display via a user device.
In some examples, the computer-readable medium may store instructions that, when executed by the processor of the computing system, cause the computing system to perform any of the methods described above.
BRIEF DESCRIPTION OF THE DRAWINGS
Reference will now be made, by way of example, to the accompanying drawings which show example embodiments of the present application, and in which:
FIG. 1 A is a block diagram of a simplified convolutional neural network, which may be used in examples of the present disclosure;
FIG. 1 B is a block diagram of a simplified transformer neural network, which may be used in examples of the present disclosure;
FIG. 2 A is a block diagram of an example computing system, which may be used to implement examples of the present disclosure;
FIG. 2 B is a block diagram illustrating an example Prompt Gener
CROSS-REFERENCE TO RELATED APPLICATIONS
The present disclosure claims priority from U.S. provisional patent No. 63/482,403, filed Jan. 31, 2023, and U.S. provisional patent No. 63/483,727, filed Feb. 7, 2023, the entireties of which are hereby incorporated by reference.
FIELD
The present disclosure relates to machine learning, and, more particularly, to generation of prompts to large language models (LLM), and, yet more particularly, to prompting an LLM to generate personalized textual descriptions.
BACKGROUND
A large language model (LLM) is a deep learning algorithm that can process natural language to summarize, translate, predict and generate text and other content. A LLM may be trained to learn billions of parameters in order to model how words relate to each other in a textual sequence. Inputs to a LLM may be referred to as prompts. A prompt is a natural language input that includes instructions to cause the LLM to generate a desired output.
SUMMARY
Conventionally, to generate a textual description of an object (e.g., a product, a location, an image, a video, a piece of music, a tangible thing, etc.), a user may prompt a LLM to generate an object description. The prompt to the LLM may be simply a list of the object attributes including the object name and optionally other attributes. The prompt may then be inputted into the LLM and the generated text may be directly outputted to the user from the LLM. However, the generated text from the LLM may be generic and static and unlikely to be 100% relevant to all users.
In various examples, the present disclosure provides a technical solution that automatically generates a prompt to cause the LLM to generate a personalized textual description using information about a user or information about a class of users as input to the LLM. In this way, text that could appeal to specific users can be automatically included in the textual description.
In various examples, the present disclosure describes systems and methods that enable a LLM to be prompted to generate a personalized textual description using user attributes (e.g., related to an individual user's preferences and needs) as inputs. Examples of the present disclosure may enable the generated personalized textual description to be automatically or semi-automatically generated, which may provide for greater efficiency and/or reduced need for user inputs. The disclosed solution may improve the performance of e-commerce platforms or merchant websites by presenting textual descriptions to specific users in a manner that is more relevant and appealing to the user.
In some examples, the present disclosure describes a computing system including: a processing unit configured to execute computer-readable instructions to cause the system to: responsive to a request for a textual description, retrieve a user record for a user associated with the request; obtain one or more user attributes based on the user record; generate a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; provide the prompt to the LLM and receive a generated user-specific textual description; and provide the generated user-specific textual description for display via a user device.
In the previous example aspect of the computing system, wherein the processing unit is configured to execute computer-readable instructions to obtain the one or more user attributes based on the user record by: extracting, by a pre-trained attribute extraction model, the one or more user attributes from the user record.
In the previous example aspect of the computing system, wherein the processing unit is configured to execute computer-readable instructions to further cause the system to: retrieve an object record for an object associated with the request; obtain one or more object attributes based on the object record; append the one or more user attributes to the one or more object attributes; and generate the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description appended to the one or more object attributes to include in the generated user-specific textual description.
In the previous example aspect of the computing system, wherein the processing unit is configured to execute computer-readable instructions to obtain the one or more object attributes by: extracting, by a pre-trained attribute extraction model, the one or more object attributes that are relevant to the user attributes, from the object record.
In the previous example aspect of the computing system, wherein in extracting the one or more object attributes and the one or more user attributes, the processing unit is configured to execute computer-readable instructions to further cause the system to: determine, by the pre-trained attribute extraction model, one or more priority object attributes from the extracted object attributes; determine, by the pre-trained attribute extraction model, one or more priority user attributes from the extracted user attributes; append the one or more priority user attributes to the one or more priority object attributes; and generate the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more priority user attributes to include in the generated user-specific textual description appended to the one or more priority object attributes to include in the generated user-specific textual description.
In some example aspects of the computing system, wherein the one or more user attributes is an embedding.
In some example aspects of the computing system, wherein the one or more object attributes is an embedding.
In some example aspects of the computing system, wherein the request is a request received from a user device to view a webpage associated with an object, and wherein the processing unit is configured to execute computer-readable instructions to further cause the system to: in response to the request, provide a modified webpage for display via the user device, the modified webpage including the user-specific textual description.
In the previous example aspect of the computing system, wherein data for the webpage stored on the system includes the source text, and wherein the modified webpage provided to the user device has the user-specific textual description substituted in real-time in response to the request.
In some example aspects of the computing system, wherein the source text is a source product description for a product associated with the request, the prompt to the LLM includes instructions to generate a user-specific product description for the product, and the generated user-specific textual description is a generated user-specific product description.
In some example aspects of the computing system, wherein the user record comprises at least one of: a current browsing activity record; a previous transaction event record; a previous browsing activity record; a previous search query; or a user profile.
In some example aspects of the computing system, wherein the user attributes include at least one of: a user demographic attribute; a user preference attribute; or a user need attribute.
In some example aspects of the computing system, wherein the processing unit is configured to execute computer-readable instructions to further cause the system to provide the prompt to the LLM as a set of tokens.
In some example aspects of the computing system, wherein the LLM is a generative pre-trained transformer LLM.
In some examples, the present disclosure describes a computer-implemented method for automatically prompting a LLM to generate a personalized text, the method includes a number of steps. The method comprising: responsive to a request for a textual description, retrieving a user record for a user associated with the request; obtaining one or more user attributes based on the user record; generating a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; providing the prompt to the LLM and receive a generated user-specific textual description; and providing the generated user-specific textual description for display via a user device.
In the previous example aspect of the method, wherein obtaining the one or more user attributes based on the user record comprises: extracting, by a pre-trained attribute extraction model, the one or more user attributes from the user record.
In the previous example aspect of the method, further comprising: prior to retrieving the user record, retrieving an object record for an object associated with the request; obtaining one or more object attributes based on the object record; and generating the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description appended to the one or more object attributes to include in the generated user-specific textual description.
In the previous example aspect of the method, wherein obtaining the one or more object attributes based on the object record comprises: extracting, by a pre-trained attribute extraction model, the one or more object attributes that are relevant to the user attributes, from the object record.
In the previous example aspect of the method, wherein extracting the one or more object attributes and the one or more user attributes comprises: determining, by the pre-trained attribute extraction model, one or more priority object attributes from the extracted object attributes; determining, by the pre-trained attribute extraction model, one or more priority user attributes from the extracted user attributes; and generating the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more priority user attributes to include in the generated user-specific textual description appended to the one or more priority object attributes to include in the generated user-specific textual description.
In an example aspect of the method, wherein the one or more user attributes is an embedding.
In an example aspect of the method, wherein the one or more object attributes is an embedding.
In an example aspect of the method, wherein the request is a request received from a user device to view a webpage associated with a product, and wherein the method further comprises: in response to the request, providing a modified webpage for display on the user device, the modified webpage including the user-specific textual description.
In the previous example aspect of the method, wherein data for the webpage stored on the system includes the source text, and wherein the modified webpage provided to the user device has the user-specific textual description substituted in real-time in response to the request.
In an example aspect of the method, wherein the source text is a source product description, the prompt to the LLM includes instructions to generate a user-specific product description for a product associated with the source product description, and the generated user-specific textual description is a generated user-specific product description.
In an example aspect of the method, wherein the user record comprises at least one of: a current browsing activity record; a previous transaction event record; a previous browsing activity record; a previous search query; or a user profile.
In an example aspect of the method, wherein the user attributes include at least one of: a user demographic attribute; a user preference attribute; or a user need attribute.
In an example aspect of the method, further comprising: providing the prompt to the LLM as a set of tokens.
In an example aspect of the method, wherein the LLM is a generative pre-trained transformer LLM.
In some examples, the present disclosure describes a computer-readable medium storing instructions that, when executed by a processor of a computing system, cause the computing system to: responsive to a request for a textual description, retrieve a user record for a user associated with the request; obtain one or more user attributes based on the user record; generate a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; provide the prompt to the LLM and receive a generated user-specific textual description; and provide the generated user-specific textual description for display via a user device.
In some examples, the computer-readable medium may store instructions that, when executed by the processor of the computing system, cause the computing system to perform any of the methods described above.
BRIEF DESCRIPTION OF THE DRAWINGS
Reference will now be made, by way of example, to the accompanying drawings which show example embodiments of the present application, and in which:
FIG. 1 A is a block diagram of a simplified convolutional neural network, which may be used in examples of the present disclosure;
FIG. 1 B is a block diagram of a simplified transformer neural network, which may be used in examples of the present disclosure;
FIG. 2 A is a block diagram of an example computing system, which may be used to implement examples of the present disclosure;
FIG. 2 B is a block diagram illustrating an example Prompt Generator, in accordance with example embodiments of the present disclosure;
FIG. 3 is a flowchart illustrating an example embodiment of a method for automatically prompting a LLM to generate a personalized textual description, in accordance with examples of the present disclosure
FIG. 4 is a flowchart illustrating an example embodiment of a method for automatically prompting a LLM to generate a personalized textual description, in accordance with examples of the present disclosure.
FIG. 5 is a block diagram of an example e-commerce platform, which may be an example implementation of the examples disclosed herein; and
FIG. 6 is an example homepage of an administrator, which may be accessed via the e-commerce platform of FIG. 5 .
Similar reference numerals may have been used in different figures to denote similar components.
DETAILED DESCRIPTION
To assist in understanding the present disclosure, some concepts relevant to neural networks and machine learning (ML) are first discussed.
Generally, a neural network comprises a number of computation units (sometimes referred to as âneuronsâ). Each neuron receives an input value and applies a function to the input to generate an output value. The function typically includes a parameter (also referred to as a âweightâ) whose value is learned through the process of training. A plurality of neurons may be organized into a neural network layer (or simply âlayerâ) and there may be multiple such layers in a neural network. The output of one layer may be provided as input to a subsequent layer. Thus, input to a neural network may be processed through a succession of layers until an output of the neural network is generated by a final layer. This is a simplistic discussion of neural networks and there may be more complex neural network designs that include feedback connections, skip connections, and/or other such possible connections between neurons and/or layers, which need not be discussed in detail here.
A deep neural network (DNN) is a type of neural network having multiple layers and/or a large number of neurons. The term DNN may encompass any neural network having multiple layers, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multilayer perceptrons (MLPs), among others.
DNNs are often used as ML-based models for modeling complex behaviors (e.g., human language, image recognition, object classification, etc.) in order to improve accuracy of outputs (e.g., more accurate predictions) such as, for example, as compared with models with fewer layers. In the present disclosure, the term âML-based modelâ or more simply âML modelâ may be understood to refer to a DNN. Training a ML model refers to a process of learning the values of the parameters (or weights) of the neurons in the layers such that the ML model is able to model the target behavior to a desired degree of accuracy. Training typically requires the use of a training dataset, which is a set of data that is relevant to the target behavior of the ML model. For example, to train a ML model that is intended to model human language (also referred to as a language model), the training dataset may be a collection of text documents, referred to as a text corpus (or simply referred to as a corpus). The corpus may represent a language domain (e.g., a single language), a subject domain (e.g., scientific papers), and/or may encompass another domain or domains, be they larger or smaller than a single language or subject domain. For example, a relatively large, multilingual and non-subject-specific corpus may be created by extracting text from online webpages and/or publicly available social media posts. In another example, to train a ML model that is intended to classify images, the training dataset may be a collection of images. Training data may be annotated with ground truth labels (e.g. each data entry in the training dataset may be paired with a label), or may be unlabeled.
Training a ML model generally involves inputting into an ML model (e.g. an untrained ML model) training data to be processed by the ML model, processing the training data using the ML model, collecting the output generated by the ML model (e.g. based on the inputted training data), and comparing the output to a desired set of target values. If the training data is labeled, the desired target values may be, e.g., the ground truth labels of the training data. If the training data is unlabeled, the desired target value may be a reconstructed (or otherwise processed) version of the corresponding ML model input (e.g., in the case of an autoencoder), or may be a measure of some target observable effect on the environment (e.g., in the case of a reinforcement learning agent). The parameters of the ML model are updated based on a difference between the generated output value and the desired target value. For example, if the value outputted by the ML model is excessively high, the parameters may be adjusted so as to lower the output value in future training iterations. An objective function is a way to quantitatively represent how close the output value is to the target value. An objective function represents a quantity (or one or more quantities) to be optimized (e.g., minimize a loss or maximize a reward) in order to bring the output value as close to the target value as possible. The goal of training the ML model typically is to minimize a loss function or maximize a reward function.
The training data may be a subset of a larger data set. For example, a data set may be split into three mutually exclusive subsets: a training set, a validation (or cross-validation) set, and a testing set. The three subsets of data may be used sequentially during ML model training. For example, the training set may be first used to train one or more ML models, each ML model, e.g., having a particular architecture, having a particular training procedure, being describable by a set of model hyperparameters, and/or otherwise being varied from the other of the one or more ML models. The validation (or cross-validation) set may then be used as input data into the trained ML models to, e.g., measure the performance of the trained ML models and/or compare performance between them. Where hyperparameters are used, a new set of hyperparameters may be determined based on the measured performance of one or more of the trained ML models, and the first step of training (i.e., with the training set) may begin again on a different ML model described by the new set of determined hyperparameters. In this way, these steps may be repeated to produce a more performant trained ML model. Once such a trained ML model is obtained (e.g., after the hyperparameters have been adjusted to achieve a desired level of performance), a third step of collecting the output generated by the trained ML model applied to the third subset (the testing set) may begin. The output generated from the testing set may be compared with the corresponding desired target values to give a final assessment of the trained ML model's accuracy. Other segmentations of the larger data set and/or schemes for using the segments for training one or more ML models are possible.
Backpropagation is an algorithm for training a ML model. Backpropagation is used to adjust (also referred to as update) the value of the parameters in the ML model, with the goal of optimizing the objective function. For example, a defined loss function is calculated by forward propagation of an input to obtain an output of the ML model and comparison of the output value with the target value. Backpropagation calculates a gradient of the loss function with respect to the parameters of the ML model, and a gradient algorithm (e.g., gradient descent) is used to update (i.e., âlearnâ) the parameters to reduce the loss function. Backpropagation is performed iteratively, so that the loss function is converged or minimized. Other techniques for learning the parameters of the ML model may be used. The process of updating (or learning) the parameters over many iterations is referred to as training. Training may be carried out iteratively until a convergence condition is met (e.g., a predefined maximum number of iterations has been performed, or the value outputted by the ML model is sufficiently converged with the desired target value), after which the ML model is considered to be sufficiently trained. The values of the learned parameters may then be fixed and the ML model may be deployed to generate output in real-world applications (also referred to as âinferenceâ).
In some examples, a trained ML model may be fine-tuned, meaning that the values of the learned parameters may be adjusted slightly in order for the ML model to better model a specific task. Fine-tuning of a ML model typically involves further training the ML model on a number of data samples (which may be smaller in number/cardinality than those used to train the model initially) that closely target the specific task. For example, a ML model for generating natural language that has been trained generically on publically-available text corpuses may be, e.g., fine-tuned by further training using the complete works of Shakespeare as training data samples (e.g., where the intended use of the ML model is generating a scene of a play or other textual content in the style of Shakespeare).
FIG. 1 A is a simplified diagram of an example CNN 10 , which is an example of a DNN that is commonly used for image processing tasks such as image classification, image analysis, object segmentation, etc. An input to the CNN 10 may be a 2D RGB image 12 .
The CNN 10 includes a plurality of layers that process the image 12 in order to generate an output, such as a predicted classification or predicted label for the image 12 . For simplicity, only a few layers of the CNN 10 are illustrated including at least one convolutional layer 14 . The convolutional layer 14 performs convolution processing, which may involve computing a dot product between the input to the convolutional layer 14 and a convolution kernel. A convolutional kernel is typically a 2D matrix of learned parameters that is applied to the input in order to extract image features. Different convolutional kernels may be applied to extract different image information, such as shape information, color information, etc.
The output of the convolution layer 14 is a set of feature maps 16 (sometimes referred to as activation maps). Each feature map 16 generally has smaller width and height than the image 12 . The set of feature maps 16 encode image features that may be processed by subsequent layers of the CNN 10 , depending on the design and intended task for the CNN 10 . In this example, a fully connected layer 18 processes the set of feature maps 16 in order to perform a classification of the image, based on the features encoded in the set of feature maps 16 . The fully connected layer 18 contains learned parameters that, when applied to the set of feature maps 16 , outputs a set of probabilities representing the likelihood that the image 12 belongs to each of a defined set of possible classes. The class having the highest probability may then be outputted as the predicted classification for the image 12 .
In general, a CNN may have different numbers and different types of layers, such as multiple convolution layers, max-pooling layers and/or a fully connected layer, among others. The parameters of the CNN may be learned through training, using data having ground truth labels specific to the desired task (e.g., class labels if the CNN is being trained for a classification task, pixel masks if the CNN is being trained for a segmentation task, text annotations if the CNN is being trained for a captioning task, etc.), as discussed above.
Some concepts in ML-based language models are now discussed. It may be noted that, while the term âlanguage modelâ has been commonly used to refer to a ML-based language model, there could exist non-ML language models. In the present disclosure, the term âlanguage modelâ may be used as shorthand for ML-based language model (i.e., a language model that is implemented using a neural network or other ML architecture), unless stated otherwise. For example, unless stated otherwise, âlanguage modelâ encompasses LLMs.
A language model may use a neural network (typically a DNN) to perform natural language processing (NLP) tasks such as language translation, image captioning, grammatical error correction, and language generation, among others. A language model may be trained to model how words relate to each other in a textual sequence, based on probabilities. A language model may contain hundreds of thousands of learned parameters or in the case of a large language model (LLM) may contain millions or billions of learned parameters or more.
In recent years, there has been interest in a type of neural network architecture, referred to as a transformer, for use as language models. For example, the Bidirectional Encoder Representations from Transformers (BERT) model, the Transformer-XL model and the Generative Pre-trained Transformer (GPT) models are types of transformers. A transformer is a type of neural network architecture that uses self-attention mechanisms in order to generate predicted output based on input data that has some sequential meaning (i.e., the order of the input data is meaningful, which is the case for most text input). Although transformer-based language models are described herein, it should be understood that the present disclosure may be applicable to any ML-based language model, including language models based on other neural network architectures such as recurrent neural network (RNN)-based language models.
FIG. 1 B is a simplified diagram of an example transformer 50 , and a simplified discussion of its operation is now provided. The transformer 50 includes an encoder 52 (which may comprise one or more encoder layers/blocks connected in series) and a decoder 54 (which may comprise one or more decoder layers/blocks connected in series). Generally, the encoder 52 and the decoder 54 each include a plurality of neural network layers, at least one of which may be a self-attention layer. The parameters of the neural network layers may be referred to as the parameters of the language model.
The transformer 50 may be trained on a text corpus that is labelled (e.g., annotated to indicate verbs, nouns, etc.) or unlabelled. LLMs may be trained on a large unlabelled corpus. Some LLMs may be trained on a large multi-language, multi-domain corpus, to enable the model to be versatile at a variety of language-based tasks such as generative tasks (e.g., generating human-like natural language responses to natural language input).
An example of how the transformer 50 may process textual input data is now described. Input to a language model (whether transformer-based or otherwise) typically is in the form of natural language as may be parsed into tokens. It should be appreciated that the term âtokenâ in the context of language models and NLP has a different meaning from the use of the same term in other contexts such as data security. Tokenization, in the context of language models and NLP, refers to the process of parsing textual input (e.g., a character, a word, a phrase, a sentence, a paragraph, etc.) into a sequence of shorter segments that are converted to numerical representations referred to as tokens (or âcompute tokensâ). Typically, a token may be an integer that corresponds to the index of a text segment (e.g., a word) in a vocabulary dataset. Often, the vocabulary dataset is arranged by frequency of use. Commonly occurring text, such as punctuation, may have a lower vocabulary index in the dataset and thus be represented by a token having a smaller integer value than less commonly occurring text. Tokens frequently correspond to words, with or without whitespace appended. In some examples, a token may correspond to a portion of a word. For example, the word âlowerâ may be represented by a token for [low] and a second token for [er]. In another example, the text sequence âCome here, look!â may be parsed into the segments [Come], [here], [,], [look] and [!], each of which may be represented by a respective numerical token. In addition to tokens that are parsed from the textual sequence (e.g., tokens that correspond to words and punctuation), there may also be special tokens to encode non-textual information. For example, a [CLASS] token may be a special token that corresponds to a classification of the textual sequence (e.g., may classify the textual sequence as a poem, a list, a paragraph, etc.), a [EOT] token may be another special token that indicates the end of the textual sequence, other tokens may provide formatting information, etc.
In FIG. 1 B , a short sequence of tokens 56 corresponding to the text sequence âCome here, look!â is illustrated as input to the transformer 50 . Tokenization of the text sequence into the tokens 56 may be performed by some pre-processing tokenization module such as, for example, a byte pair encoding tokenizer (the âpreâ referring to the tokenization occurring prior to the processing of the tokenized input by the LLM), which is not shown in FIG. 1 B for simplicity. In general, the token sequence that is inputted to the transformer 50 may be of any length up to a maximum length defined based on the dimensions of the transformer 50 (e.g., such a limit may be 2048 tokens in some LLMs). Each token 56 in the token sequence is converted into an embedding vector 60 (also referred to simply as an embedding). An embedding 60 is a learned numerical representation (such as, for example, a vector) of a token that captures some semantic meaning of the text segment represented by the token 56 . The embedding 60 represents the text segment corresponding to the token 56 in a way such that embeddings corresponding to semantically-related text are closer to each other in a vector space than embeddings corresponding to semantically-unrelated text. For example, assuming that the words âlookâ, âseeâ, and âcakeâ each correspond to, respectively, a âlookâ token, a âseeâ token, and a âcakeâ token when tokenized, the embedding 60 corresponding to the âlookâ token will be closer to another embedding corresponding to the âseeâ token in the vector space, as compared to the distance between the embedding 60 corresponding to the âlookâ token and another embedding corresponding to the âcakeâ token. The vector space may be defined by the dimensions and values of the embedding vectors. Various techniques may be used to convert a token 56 to an embedding 60 . For example, another trained ML model may be used to convert the token 56 into an embedding 60 . In particular, another trained ML model may be used to convert the token 56 into an embedding 60 in a way that encodes additional information into the embedding 60 (e.g., a trained ML model may encode positional information about the position of the token 56 in the text sequence into the embedding 60 ). In some examples, the numerical value of the token 56 may be used to look up the corresponding embedding in an embedding matrix 58 (which may be learned during training of the transformer 50 ).
The generated embeddings 60 are input into the encoder 52 . The encoder 52 serves to encode the embeddings 60 into feature vectors 62 that represent the latent features of the embeddings 60 . The encoder 52 may encode positional information (i.e., information about the sequence of the input) in the feature vectors 62 . The feature vectors 62 may have very high dimensionality (e.g., on the order of thousands or tens of thousands), with each element in a feature vector 62 corresponding to a respective feature. The numerical weight of each element in a feature vector 62 represents the importance of the corresponding feature. The space of all possible feature vectors 62 that can be generated by the encoder 52 may be referred to as the latent space or feature space.
Conceptually, the decoder 54 is designed to map the features represented by the feature vectors 62 into meaningful output, which may depend on the task that was assigned to the transformer 50 . For example, if the transformer 50 is used for a translation task, the decoder 54 may map the feature vectors 62 into text output in a target language different from the language of the original tokens 56 . Generally, in a generative language model, the decoder 54 serves to decode the feature vectors 62 into a sequence of tokens. The decoder 54 may generate output tokens 64 one by one. Each output token 64 may be fed back as input to the decoder 54 in order to generate the next output token 64 . By feeding back the generated output and applying self-attention, the decoder 54 is able to generate a sequence of output tokens 64 that has sequential meaning (e.g., the resulting output text sequence is understandable as a sentence and obeys grammatical rules). The decoder 54 may generate output tokens 64 until a special [EOT] token (indicating the end of the text) is generated. The resulting sequence of output tokens 64 may then be converted to a text sequence in post-processing. For example, each output token 64 may be an integer number that corresponds to a vocabulary index. By looking up the text segment using the vocabulary index, the text segment corresponding to each output token 64 can be retrieved, the text segments can be concatenated together and the final output text sequence (in this example, âViens ici, regarde!â) can be obtained.
Although a general transformer architecture for a language model and its theory of operation have been described above, this is not intended to be limiting. Existing language models include language models that are based only on the encoder of the transformer or only on the decoder of the transformer. An encoder-only language model encodes the input text sequence into feature vectors that can then be further processed by a task-specific layer (e.g., a classification layer). BERT is an example of a language model that may be considered to be an encoder-only language model. A decoder-only language model accepts embeddings as input and may use auto-regression to generate an output text sequence. Transformer-XL and GPT-type models may be language models that are considered to be decoder-only language models.
Because GPT-type language models tend to have a large number of parameters, these language models may be considered LLMs. An example GPT-type LLM is GPT-3. GPT-3 is a type of GPT language model that has been trained (in an unsupervised manner) on a large corpus derived from documents available to the public online. GPT-3 has a very large number of learned parameters (on the order of hundreds of billions), is able to accept a large number of tokens as input (e.g., up to 2048 input tokens), and is able to generate a large number of tokens as output (e.g., up to 2048 tokens). GPT-3 has been trained as a generative model, meaning that it can process input text sequences to predictively generate a meaningful output text sequence. ChatGPT is built on top of a GPT-type LLM, and has been fine-tuned with training datasets based on text-based chats (e.g., chatbot conversations). ChatGPT is designed for processing natural language, receiving chat-like inputs and generating chat-like outputs.
A computing system may access a remote language model (e.g., a cloud-based language model), such as ChatGPT or GPT-3, via a software interface (e.g., an application programming interface (API)). Additionally or alternatively, such a remote language model may be accessed via a network such as, for example, the Internet. In some implementations such as, for example, potentially in the case of a cloud-based language model, a remote language model may be hosted by a computer system as may include a plurality of cooperating (e.g., cooperating via a network) computer systems such as may be in, for example, a distributed arrangement. Notably, a remote language model may employ a plurality of processors (e.g., hardware processors such as, for example, processors of cooperating computer systems). Indeed, processing of inputs by an LLM may be computationally expensive/may involve a large number of operations (e.g., many instructions may be executed/large data structures may be accessed from memory) and providing output in a required timeframe (e.g., real-time or near real-time) may require the use of a plurality of processors/cooperating computing devices as discussed above.
Inputs to an LLM may be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computing system may generate a prompt that is provided as input to the LLM via its API. As described above, the prompt may optionally be processed or pre-processed into a token sequence prior to being provided as input to the LLM via its API. A prompt can include one or more examples of the desired output, which provides the LLM with additional information to enable the LLM to better generate output according to the desired output. Additionally or alternatively, the examples included in a prompt may provide inputs (e.g., example inputs) corresponding to/as may be expected to result in the desired outputs provided. A one-shot prompt refers to a prompt that includes one example, and a few-shot prompt refers to a prompt that includes multiple examples. A prompt that includes no examples may be referred to as a zero-shot prompt.
FIG. 2 A illustrates an example computing system 400 , which may be used to implement examples of the present disclosure, such as a prompt generation engine to generate prompts to be provided as input to a language model such as a LLM. Additionally or alternatively, one or more instances of the example computing system 400 may be employed to execute the LLM. For example, a plurality of instances of the example computing system 400 may cooperate to provide output using an LLM in manners as discussed above.
The example computing system 400 includes at least one processing unit, such as a processor 402 , and at least one physical memory 404 . The processor 402 may be, for example, a central processing unit, a microprocessor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a dedicated logic circuitry, a dedicated artificial intelligence processor unit, a graphics processing unit (GPU), a tensor processing unit (TPU), a neural processing unit (NPU), a hardware accelerator, or combinations thereof. The memory 404 may include a volatile or non-volatile memory (e.g., a flash memory, a random access memory (RAM), and/or a read-only memory (ROM)). The memory 404 may store instructions for execution by the processor 402 , to the computing system 400 to carry out examples of the methods, functionalities, systems and modules disclosed he
CLAIMS
Claims ( 24 )
1 . A computer system comprising:
a processing unit configured to execute computer-readable instructions to cause the system to:
responsive to a request for a textual description, retrieve a user record for a user associated with the request;
obtain one or more user attributes based on the user record;
generate a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text;
provide the prompt to the LLM and receive a generated user-specific textual description; and
provide the generated user-specific textual description for display via a user device.
2 . The system of claim 1 , wherein the processing unit is configured to execute computer-readable instructions to obtain the one or more user attributes based on the user record by:
extracting, by a pre-trained attribute extraction model, the one or more user attributes from the user record.
3 . The system of claim 2 , wherein the processing unit is configured to execute computer-readable instructions to further cause the system to:
retrieve an object record for an object associated with the request; obtain one or more object attributes based on the object record; append the one or more user attributes to the one or more object attributes; and generate the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description appended to the one or more object attributes to include in the generated user-specific textual description.
4 . The system of claim 3 , wherein the processing unit is configured to execute computer-readable instructions to obtain the one or more object attributes by:
extracting, by a pre-trained attribute extraction model, the one or more object attributes that are relevant to the user attributes, from the object record.
5 . The system of claim 4 , wherein in extracting the one or more object attributes and the one or more user attributes, the processing unit is configured to execute computer-readable instructions to further cause the system to:
determine, by the pre-trained attribute extraction model, one or more priority object attributes from the extracted object attributes; determine, by the pre-trained attribute extraction model, one or more priority user attributes from the extracted user attributes; append the one or more priority user attributes to the one or more priority object attributes; and generate the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more priority user attributes to include in the generated user-specific textual description appended to the one or more priority object attributes to include in the generated user-specific textual description.
6 . The system of claim 1 , wherein the one or more user attributes is an embedding.
7 . The system of claim 3 , wherein the one or more object attributes is an embedding.
8 . The system of claim 1 , wherein the source text is a source product description for a product associated with the request, the prompt to the LLM includes instructions to generate a user-specific product description for the product, and the generated user-specific textual description is a generated user-specific product description.
9 . The system of claim 1 , wherein the user record comprises at least one of:
a current browsing activity record; a previous transaction event record; a previous browsing activity record; a previous search query; or a user profile.
10 . The system of claim 1 , wherein the user attributes include at least one of:
a user demographic attribute; a user preference attribute; or a user need attribute.
11 . The system of claim 1 , wherein the processing unit is configured to execute computer-readable instructions to further cause the system to provide the prompt to the LLM as a set of tokens.
12 . The system of claim 1 , wherein the LLM is a generative pre-trained transformer LLM.
13 . A method comprising:
responsive to a request for a textual description, retrieving a user record for a user associated with the request;
obtaining one or more user attributes based on the user record;
generating a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text;
providing the prompt to the LLM and receive a generated user-specific textual description; and
providing the generated user-specific textual description for display via a user device.
14 . The method of claim 13 , wherein obtaining the one or more user attributes based on the user record comprises:
extracting, by a pre-trained attribute extraction model, the one or more user attributes from the user record.
15 . The method of claim 14 , further comprising:
prior to retrieving the user record, retrieving an object record for an object associated with the request; obtaining one or more object attributes based on the object record; and generating the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description appended to the one or more object attributes to include in the generated user-specific textual description.
16 . The method of claim 15 , wherein obtaining the one or more object attributes based on the object record comprises:
extracting, by a pre-trained attribute extraction model, the one or more object attributes that are relevant to the user attributes, from the object record.
17 . The method of claim 16 , wherein extracting the one or more object attributes and the one or more user attributes comprises:
determining, by the pre-trained attribute extraction model, one or more priority object attributes from the extracted object attributes; determining, by the pre-trained attribute extraction model, one or more priority user attributes from the extracted user attributes; and generating the prompt to the LLM for generating the user-specific textual description, the prompt including the one or more priority user attributes to include in the generated user-specific textual description appended to the one or more priority object attributes to include in the generated user-specific textual description.
18 . The method of claim 13 , wherein the one or more user attributes is an embedding.
19 . The method of claim 15 , wherein the one or more object attributes is an embedding.
20 . The method of claim 13 , wherein the request is a request received from a user device to view a webpage associated with a product, and wherein the method further comprises:
in response to the request, providing a modified webpage for display on the user device, the modified webpage including the user-specific textual description.
21 . The method of claim 20 , wherein data for the webpage stored on the system includes the source text, and wherein the modified webpage provided to the user device has the user-specific textual description substituted in real-time in response to the request.
22 . The method of claim 13 , wherein the source text is a source product description, the prompt to the LLM includes instructions to generate a user-specific product description for a product associated with the source product description, and the generated user-specific textual description is a generated user-specific product description.
23 . The method of claim 13 , wherein the user record comprises at least one of:
a current browsing activity record; a previous transaction event record; a previous browsing activity record; a previous search query; or a user profile.
24 . A computer-readable medium storing instructions that, when executed by a processor of a computing system, cause the computing system to:
responsive to a request for a textual description, retrieve a user record for a user associated with the request; obtain one or more user attributes based on the user record; generate a prompt to a large language model (LLM) for generating a user-specific textual description, the prompt including the one or more user attributes to include in the generated user-specific textual description and a source text; provide the prompt to the LLM and receive a generated user-specific textual description; and provide the generated user-specific textual description for display via a user device.
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