ADAPTING A LANGUAGE MODEL FOR MULTIMODAL MULTI-TASK LEARNING

A method, apparatus and system for adapting a language model for understanding domain-specific multimodal content include acquiring domain-specific multimodal content for at least one content domain and applying question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content for the at least one domain. As such, the trained language model can be implemented to answer questions directed to the domain-specific multimodal content for the at least one domain.

FIELD

Embodiments of the present principles generally relate to task learning using language models and, more particularly, to a method, apparatus and system for the adaptation of large language models for in-context learning in multimodal domains.

BACKGROUND

Content understanding today consists of implementing language models to answer questions about/using the content. Recent large language models such as GPT-3 are able to generalize knowledge obtained from content to new tasks, however for narrow tasks, fail to truly understand the content. That is, for specific tasks, state of the art language models are functionally “stochastic parrots” or “smart/super parrots” that simply memorize without deeper comprehension. That is, current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. In addition, language models are typically trained and respond to questions with reference to only text/word content and not multimodal content.

SUMMARY

Embodiments of methods, apparatuses and systems for adapting a language model for understanding domain-specific multimodal content are disclosed herein.

In some embodiments, a method for adapting a language model for understanding domain-specific multimodal content includes acquiring domain-specific multimodal content for at least one content domain, and applying question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, the method further includes using the trained language model to answer questions directed to the domain-specific multimodal content for the at least one domain.

In some embodiments, a non-transitory machine-readable medium has stored thereon at least one program, the at least one program including instructions which, when executed by a processor, cause the processor to perform a method in a processor-based system for adapting a language model for understanding domain-specific multimodal content including acquiring domain-specific multimodal content for at least one content domain, and applying question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, the method of the non-transitory machine-readable medium further includes using the trained language model to answer questions directed to the domain-specific multimodal content for the at least one domain.

In some embodiments, an apparatus for adapting a language model for understanding domain-specific multimodal content includes a knowledge acquisition module, a task learning module, a processor, and a memory accessible to the processor, the memory having stored therein at least one of programs or instructions. In some embodiments, when the programs or instructions are executed by the processor, the apparatus is configured to acquire, using the knowledge acquisition module, domain-specific multimodal content for at least one content domain, and apply, using the task learning module, question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, the apparatus is further configured to use the trained language model to answer questions directed to the domain-specific multimodal content for the at least one domain.

In some embodiments, a computer-implemented method for training a language model for understanding domain-specific multimodal content includes acquiring a set of domain-specific multimodal content data for at least one content domain, using a machine learning model, creating a set of question/answer pairs to apply to the domain-specific multimodal content data, creating a training set comprising the acquired set of domain-specific multimodal content data and the created question/answer pairs, and training the language model using the training set by applying the question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, a method for implementing a trained language model to answer inquiries directed to domain-specific multimodal content for the at least one domain includes receiving an inquiry directed at the domain-specific multimodal content and providing a response to the inquiry using the trained language model. In some embodiments, the language model is trained by acquiring a set of domain-specific multimodal content data for at least one content domain, using a machine learning model, creating a set of question/answer pairs to apply to the domain-specific multimodal content data, creating a training set comprising the acquired set of domain-specific multimodal content data and the created question/answer pairs, and training the language model using the training set by applying the question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

Other and further embodiments in accordance with the present principles are described below.

DETAILED DESCRIPTION

Embodiments of the present principles generally relate to methods, apparatuses and systems for adaptation of language models for in-context learning in multimodal domains. That is, embodiments of the present principles provide methods, apparatus and systems for adapting a language model for understanding domain-specific multimodal content by training a language model to learn tasks associated with domain-specific multimodal content. While the concepts of the present principles are susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are described in detail below. It should be understood that there is no intent to limit the concepts of the present principles to the particular forms disclosed. On the contrary, the intent is to cover all modifications, equivalents, and alternatives consistent with the present principles and the appended claims. For example, although embodiments of the present principles will be described primarily with respect to implementing specific question/answer pairs associated with particular domain-specific multimodal content for the adaptation and training of language models, such teachings should not be considered limiting. Embodiments in accordance with the present principles can function with substantially any question/answer pairs associated with other domain-specific multimodal content for the adaptation and training of language models.

Throughout the teachings herein, the phrase “question/answer pair” is used to define a data pair that describes an inquiry regarding data and a solution to that inquiry. Such data pair can be used to train a model as described herein. Although embodiments of the present principles described herein implement the phrase “question/answer pair”, alternatively or in addition, in some embodiments, the phrase “prompt/response” can also be used in addition to and/or in place of the phrase “question/answer pair” to describe the data pair that describes the inquiry regarding data and the solution to that inquiry.

Embodiments of the present principles provide a method, apparatus and system for adapting language models, such as Large Language Models (LLMs), to understand and then answer questions for focused domains, which can be performed on-the-fly. In some embodiments, an LLM is adapted by only adapting a few parameters (which can be considered adapters). In some specific embodiments, a novel approach disentangles the adaptation of an LLM to a new domain into Knowledge Acquisition and Task Learning/training for efficient learning. Such an adaption of the present principles can be very useful for domains in which it is hard to get large amounts of data.

FIG.1depicts a high-level block diagram of a multi-modal language model adapter100in accordance with an embodiment of the present principles. The multi-modal language model adapter100ofFIG.1illustratively comprises a knowledge acquisition module110, a task learning module120, and an optional storage device130. In the embodiment ofFIG.1, the task learning module120of the multi-modal language model adapter100can include a machine learning system140. In the embodiment ofFIG.1, the multi-modal language model adapter100is in communication with an LLM150for purposes of adapting/training the LLM150to perform specific tasks using domain-specific multimodal language. Although in the embodiment of the multi-modal language model adapter100ofFIG.1the LLM150is depicted as a separate component from the multi-modal language model adapter100, in some embodiments of the present principles, the LLM150can be included as a component of a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1.

As further depicted inFIG.1, embodiments of a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, can be implemented via a computing device600in accordance with the present principles (described in greater detail below with reference toFIG.6).

FIG.2depicts a functional diagram200of a multi-modal language model adapter100in accordance with an embodiment of the present principles. In the embodiment ofFIG.2, multimodal content/documents210(e.g., images, text, audio, videos, etc., and any combination thereof) can be acquired by a knowledge acquisition module of the present principles, such as the knowledge acquisition module110of the multi-modal language model adapter100ofFIG.1. In some embodiments, multimodal content210specific to at least one domain (in some embodiments two or three or more domains) can be received by the knowledge acquisition module110, the domain-specific content intended to adapt an associated language model, such as a Large Language Model (LLM), to understand and/or answer questions for the focused domain(s) represented by the acquired multimodal content in accordance with the present principles.

In some embodiments and as depicted inFIG.2, the multimodal content can be acquired by the knowledge acquisition module110from the optional storage device130. That is, in some embodiments the optional storage device130can be configured to, upon prompting, communicate multimodal content of one or more specific content domains to the knowledge acquisition module110of the multi-modal language model adapter100. For example, in some embodiments, a user can implement an input device of, for example, the computing device600, to communicate with the optional storage device130to prompt the storage device to communicate multimodal content of one or more specific domains to the knowledge acquisition module110of the multimodal language model adapter100ofFIG.1. Embodiments of the present principles adapt a typically text-only language model, such as an LLM, into a multimodal language model using the acquired domain-specific, multimodal content in accordance with the present principles and as described in further detail below. Although the embodiment ofFIG.2is described above as acquiring multimodal content from the optional storage device130, in some embodiments of the present principles, domain-specific multimodal content can be acquired from other sources of multimodal content, such as from user input.

Alternatively or in addition, in some embodiments of the present principles, the knowledge acquisition module110of the multimodal language model adapter100ofFIG.1can search the LLM150to acquire content (multimodal or otherwise) of a particular domain(s) to adapt the LLM150to understand and/or answer questions for the particular domain(s) represented by the acquired content in accordance with the present principles. Although typically LLMs only contain single mode (textual) content, in embodiments in which an LLM contains multimodal content, embodiments of the present principles can use multimodal content acquired from the LLM to adapt the LLM to understand and/or answer questions for the particular domain(s) represented by the acquired multimodal documents in accordance with the present principles (described in greater detail below).

In embodiments of the present principles, the domain-specific content acquired by a knowledge acquisition module of the present principles, such as the knowledge acquisition module110of the multimodal language model adapter100ofFIG.1, can be conditioned by, for example a knowledge acquisition module of the present principles, for enabling the application of question/answer pairs to the domain-specific content to train a language model, such as an LLM, to perform tasks directed to the domain-specific content. For example, in some embodiments domain-specific tools for extracting content can be implemented by a knowledge acquisition module of the present principles, such as the knowledge acquisition module110of the multimodal language model adapter100ofFIG.1. That is, in some embodiments to extract domain-specific relevant data (images, text, audio, video, and/or a combination thereof) from acquired pdf documents, a knowledge acquisition module of the present principles can implement a pypdf2 tool, which includes a free and open-source pure-python PDF library capable of splitting, merging, cropping, and transforming the pages of PDF files and can also add custom data, viewing options, and passwords to PDF files.

Once the relevant data is obtained, off-the-shelf cleaning tools, such as clean-text can be implemented by, for example a knowledge acquisition module of the present principles, to preprocess and clean the text. That is, clean text is human language rearranged into a format that machine models can understand and can be performed using a simple Python code that eliminates stopwords, removes unicode words, and simplifies complex words to their root form. In addition, in some embodiments, images and their corresponding text can also be associated using heuristics or tools such as unstructured.io for parsing the pdf document into relevant parts such as, image and associated captions, paragraph title etc. That is, unstructured.io provides libraries with open-source components for pre-processing text documents such as PDFs, HTML and Word Documents. The above described tools and applications represent only an example of tools that can be implemented in accordance with the present principles to condition acquired domain-specific content and should not be considered limiting.

Referring back toFIG.1andFIG.2, in some embodiments of the present principles, the knowledge acquired by the knowledge acquisition module110of the multi-modal language model adapter100ofFIG.1can be used by the task learning module120to train the LLM150to learn tasks. For example, in some embodiments, the task learning module120can apply a question/answering process (e.g., an autodidact process) to multimodal content acquired by the knowledge acquisition module110to train the LLM150to learn tasks associated with the acquired, domain-specific multimodal content.

For example, in some embodiments of the present principles, the task learning module120of the multi-modal language model adapter100ofFIG.1can implement an in-context learning approach to adapt an LLM, such as the LLM150ofFIG.1, to few-shot learning of tasks associated with the acquired, task-specific multimodal content. That is, embodiments of the present principles expand the concepts of textual in-context learning to multimodal in-context learning of language models, such as the LLM150ofFIG.1. In some embodiments, at test time, a task learning module of the present principles, such as the task learning module120of the multi-modal language model adapter100ofFIG.1, can provide examples of multimodal task performances, which can include acquired task-specific multimodal content and associated labels, to train the LLM150using the example multimodal content (e.g., images) and, for example, question/answer pairs. In such embodiments, the LLM150learns the task at hand through the example multimodal content (e.g., text, images, audio, videos, etc., and any combination thereof) provided at test time (i.e., the LLM150adapts to the task at hand on the fly at test time by learning from the examples provided at test time). As such, embodiments of the present principles enable an LLM, such as the LLM150to adapt to any multimodal task as long as a few examples (i.e., question/answer pairs for domain-specific multimodal content) are provided at test time.

For example,FIG.3depicts an illustrative example of a multi-modal, in-context learning approach including a question/answering process300(e.g., an autodidact process) to be applied to domain-specific multimodal content acquired by a knowledge acquisition module of the present principles, and specifically associated with preparing a dish according to a recipe, to train a language model, such as the LLM150, to learn tasks associated with the preparation of a recipe in accordance with an embodiment of the present principles. In the embodiment depicted inFIG.3, the questions applied to the multimodal content comprise questions of increasing task complexity. That is, in some embodiments of the present principles, the questions of the question/answer pairs can comprise layers of at least one hierarchical taxonomy, which can include a Bloom's taxonomy.

Specifically, in the embodiment ofFIG.3, a first question310applied to an image of a video clip of a woman in a kitchen recites “Provide a caption for this video clip?”. The first question310can be classified as a captioning question. In accordance with the present principles, the answer “The woman is introducing the recipe”312is provided for the first question310for the image of a video clip of a woman in a kitchen to train the LLM150to learn the task. In the embodiment ofFIG.3, a second question315applied to the task specific multimodal content recites “What is the correct order of the images?”. The second question315is a little higher in task complexity and is classified as an ordering question. In accordance with the present principles, the answer “Second image comes before first image”317is provided for the second question315.

In the embodiment ofFIG.3, a third question320applied to the task specific multimodal content recites “Predict the recipe”. The third question320is still a little higher in task complexity and is classified as a prediction question. In accordance with the present principles, the answer “Mix vegetable saute”322is provided for the third question320. In the embodiment ofFIG.3, a fourth question325applied to the task specific multimodal content recites “Do the clip and caption match?”. The fourth question325is still a little higher in task complexity and is classified as multimodal matching question. Similar to the first310, the second315, and the third320questions, embodiments of the present principles provide an answer “Yes”327to the fourth325question to train an LLM, such as the LLM150, to learn tasks to adapt the LLM150to understand and/or answer questions for the particular domain(s) represented by the multimodal content in accordance with the present principles.

In some embodiments, question/answer pairs of the present principles can be predetermined and stored in a memory accessible to at least the task learning module120to be available for application to acquired content for training an LLM as described above. Alternatively or in addition, in some embodiments, question/answer pairs to be applied to domain-specific multimodal content in accordance with the present principles can be received by a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, along with domain specific multimodal content to which the question/answer pairs are to be applied.

Referring back to the multi-modal language model adapter100ofFIG.1, in some embodiments, the task learning module120can include a machine learning system140. The machine learning system140of the task learning module120can be trained to identify appropriate question/answer pairs to apply to multimodal content based on the multimodal content acquired by the knowledge acquisition module110. That is, in some embodiments of the present principles, the machine learning system140of the task learning module120can include a multi-layer neural network comprising nodes that are trained to have specific weights and biases. In some embodiments, the machine learning system140can employ artificial intelligence techniques or machine learning techniques to analyze domain-specific multimodal content to identify appropriate question/answer pairs to apply to the content. In some embodiments in accordance with the present principles, suitable machine learning techniques can be applied to learn commonalities in sequential application programs and for determining from the machine learning techniques at what level sequential application programs can be canonicalized. In some embodiments, machine learning techniques that can be applied to learn commonalities in sequential application programs can include, but are not limited to, regression methods, ensemble methods, or neural networks and deep learning such as ‘Seq2Seq’ Recurrent Neural Network (RNNs)/Long Short-Term Memory (LSTM) networks, Convolution Neural Networks (CNNs), graph neural networks applied to the abstract syntax trees corresponding to the sequential program application, and the like. In some embodiments a supervised machine learning (ML) classifier/algorithm could be used such as, but not limited to, Multilayer Perceptron, Random Forest, Naive Bayes, Support Vector Machine, Logistic Regression and the like. In addition, in some embodiments, the ML classifier/algorithm of the present principles can implement at least one of a sliding window or sequence-based techniques to analyze data content.

In some embodiments, the machine learning system140of the task learning module120ofFIG.1can be trained using a plurality (e.g., hundreds, thousands, millions, etc.) of instances of question/answer pairs and associated domain-specific multimodal content to which the question/answer pairs apply for training a model/algorithm of the present principles for automatically applying question/answer pairs to acquired domain-specific multimodal content to train an LLM to perform tasks related to the domain-specific multimodal content to adapt the LLM to understand and answer questions for focused domains. In such embodiments of the present principles, the model can be trained to associate question/answer pairs that best flush out the composition of the domain-specific multimodal content to be applied to the multimodal content during training of an LLM. That is, once a model is trained, the task learning module120can apply the model to automatically determine question/answer pairs to apply to the acquired domain-specific multimodal content based on the composition of the multimodal content.

Although in the embodiment of the multi-modal language model adapter100ofFIG.1, the task learning module120illustratively includes a machine learning system140, which can be trained to identify appropriate question/answer pairs to apply to multimodal content, alternatively or in addition, in some embodiments a language model, such as the LLM150ofFIG.1, can be trained to identify appropriate question/answer pairs to apply to multimodal content in accordance with the present principles and as described above.

In some embodiments of the present principles, a conceptual consistency process can be used by a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, to improve knowledge acquisition. That is, in some embodiments, conceptual consistency is used to measure the LLM's150understanding of relevant concepts. A resultant metric measures how well a language model can be characterized by finding out how consistent the responses of the language model are to queries about conceptually relevant background knowledge. Using such information, a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, can determine if further training is needed to increase a consistency of responses of the language model to queries. In such embodiments, if further training is required, additional question/answer pairs can be implemented to train the language model. As such, embodiments of the present principles can further train a language model to learn to learn from a few examples what is the target task and how to carry it out (i.e., the atomic concept of the task). For example, if examples of sorting animals and buildings into separate categories are presented by a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, to an LLM at test time, the LLM can be trained to learn the underlying task of separating objects/groups into categories and can learn to separate images of, for example, birds and humans into different categories.

Once trained, a language model trained in accordance with the present principles can be implemented to understand and answer questions directed to the acquired, domain-specific multimodal content for at least one domain associated with the domain-specific multimodal content. For example,FIG.4depicts a functional diagram of an LLM, such as the LLM150ofFIG.1, having been trained and adapted by a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, to learn the task of making curry in accordance with an embodiment of the present principles. As depicted inFIG.4, upon a user of the LLM150entering a first question410“How do I make my curry less spicy?”, the LLM150, having been adapted by a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, to learn the task of making curry, responds with the phrase460“Adding dairy is a good way to make curry less spicy”. As depicted inFIG.4, the first question410and response460are considered implicit. In the embodiment ofFIG.4, upon a user of the LLM150entering a second question415“What kind of dairy should I add to make curry less spicy?”, the LLM150adapted in accordance with the present principles responds with the phrase465“Most work well, but yogurt is preferred in Indian curries while coconut milk is preferred in Thai curries”. As depicted inFIG.4, the second question415and response465are considered a little less implicit and more explicit.

In the embodiment ofFIG.4, upon a user of the LLM150entering a third question420“When do I add coconut milk to Thai curry?”, the adapted LLM150responds with the phrase470“Coconut milk is usually added during the end of making Thai curry”. As depicted in the embodiment ofFIG.4, the third question420and response470are considered even a little less implicit and more explicit. Furthermore, in the embodiment ofFIG.4, upon a user of the LLM150entering a fourth question425“After which step do I add coconut milk to Thai curry?”, the adapted LLM150responds with the phrase475“Add it after the meat and vegetables are cooked, soon before serving”. As depicted in the embodiment ofFIG.4, the fourth question425and response475are considered more explicit than implicit.

Finally, in in the embodiment ofFIG.4, upon a user of the LLM150entering a fifth question430“Is this the right color for Thai curry?” and including an image, the adapted LLM150responds with the phrase480“The color is good, but it looks too oily”. As depicted in the embodiment ofFIG.4, the fifth question430and response480are explicit. As depicted in the embodiment ofFIG.4, an LLM, adapted in accordance with the present principles, is able to understand and then answer questions for focused domains, having been adapted/trained using domain-specific multimodal content.

FIG.5depicts a flow diagram of a method500for adapting a language model for understanding domain-specific multimodal content in accordance with an embodiment of the present principles. The method500can begin at502during which domain-specific multimodal content is acquired for at least one content domain. For example and as described above, in some embodiments a knowledge acquisition module of the present principles, such as the knowledge acquisition module110ofFIG.1, can acquire domain-specific multimodal content from a storage device and/or from an associated language model. The method500can proceed to504.

At504, question/answer pairs are applied to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content. For example and as described above, in some embodiments a task learning module of the present principles, such as the learning module120ofFIG.1, applies question/answer pairs to the acquired, domain-specific multimodal content to adapt the associated language model to learn a task(s) associated with the acquired, domain-specific multimodal content, which enables the language model to understand and answer questions about the acquired, domain-specific multimodal content. That is, once the language model is adapted in accordance with the present principles, the language model is able to understand the domain-specific multimodal content and provide responses to prompts/tasks intended to be fulfilled using the content (and specifically the domain-specific multimodal content) accessible by the language model. The method500can then be exited at506.

In some embodiments, the method500can further include using the trained language model to answer questions directed to the domain-specific multimodal content for the at least one domain.

In some embodiments, the method can further include using an in-context multimodal learning approach to train the language model to learn the tasks associated with the domain-specific multimodal content.

In some embodiments, in the method the domain-specific multimodal content is acquired from at least one of a storage device, a user input, or the language model.

In some embodiments, in the method the question/answer pairs are automatically selected and applied to the acquired, domain-specific multimodal content based on a composition of the acquired, domain-specific multimodal content.

In some embodiments, in the method, which question/answer pairs to apply to the acquired, domain-specific multimodal content are determined using a trained machine learning process.

In some embodiments, in the method the question/answer pairs applied to the acquired, domain-specific multimodal content comprise varying levels of complexity. In such embodiments, the question/answer pairs are applied to the acquired, domain-specific multimodal content as respective layers of at least one hierarchical taxonomy.

In some embodiments, a non-transitory machine-readable medium having stored thereon at least one program, the at least one program including instructions which, when executed by a processor, cause the processor to perform a method in a processor-based system for adapting a language model for understanding domain-specific multimodal content, including acquiring domain-specific multimodal content for at least one content domain, and applying question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, the method performed further includes using the trained language model to answer questions directed to the domain-specific multimodal content for the at least one domain.

In some embodiments, the method performed further includes using an in-context multimodal learning approach to train the language model to learn the tasks associated with the domain-specific multimodal content.

In some embodiments, in the method performed the domain-specific multimodal content is acquired from at least one of the non-transitory machine-readable medium, a user input, or the language model.

In some embodiments, in the method performed the question/answer pairs are automatically selected and applied to the acquired, domain-specific multimodal content based on a composition of the acquired, domain-specific multimodal content.

In some embodiments, in the method performed, which question/answer pairs to apply to the acquired, domain-specific multimodal content are determined using a trained machine learning process.

In some embodiments, in the method performed, the question/answer pairs applied to the acquired, domain-specific multimodal content comprise varying levels of complexity.

In some embodiments, in the method performed the question/answer pairs are applied to the acquired, domain-specific multimodal content as respective layers of at least one hierarchical taxonomy.

In some embodiments, an apparatus for adapting a language model for understanding domain-specific multimodal content includes a knowledge acquisition module, a task learning module, a processor, and a memory accessible to the processor, the memory having stored therein at least one of programs or instructions. In some embodiments, when the programs or instructions are executed by the processor, the apparatus is configured to acquire, using the knowledge acquisition module, domain-specific multimodal content for at least one content domain, and apply, using the task learning module, question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, the apparatus is further configured to use the trained language model to answer questions directed to the domain-specific multimodal content for the at least one domain.

In some embodiments, the apparatus is further configured to use an in-context multimodal learning approach to train the language model to learn the tasks associated with the domain-specific multimodal content.

In some embodiments, the domain-specific multimodal content is acquired from at least one of the non-transitory machine-readable medium, a user input, or the language model.

In some embodiments, the question/answer pairs are automatically selected and applied to the acquired, domain-specific multimodal content based on a composition of the acquired, domain-specific multimodal content.

In some embodiments, which question/answer pairs to apply to the acquired, domain-specific multimodal content are determined using a trained machine learning process. In some embodiments, the question/answer pairs applied to the acquired, domain-specific multimodal content comprise varying levels of complexity. In some embodiments, the question/answer pairs are applied to the acquired, domain-specific multimodal content as respective layers of at least one hierarchical taxonomy.

In some embodiments, a computer-implemented method for training a language model for understanding domain-specific multimodal content includes acquiring a set of domain-specific multimodal content data for at least one content domain, using a machine learning model, creating a set of question/answer pairs to apply to the domain-specific multimodal content data, creating a training set comprising the acquired set of domain-specific multimodal content data and the created question/answer pairs, and training the language model using the training set by applying the question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

In some embodiments, a method for implementing a trained language model to answer inquiries directed to domain-specific multimodal content for the at least one domain includes receiving an inquiry directed at the domain-specific multimodal content; and providing a response to the inquiry using the trained language model, the language model having been trained by acquiring a set of domain-specific multimodal content data for at least one content domain, using a machine learning model, creating a set of question/answer pairs to apply to the domain-specific multimodal content data, creating a training set comprising the acquired set of domain-specific multimodal content data and the created question/answer pairs, and training the language model using the training set by applying the question/answer pairs to the acquired, domain-specific multimodal content for the at least one content domain to train the language model to learn tasks associated with the domain-specific multimodal content.

Embodiments of the present principles advantageously provide rapid adaptation of language models, such as LLMs, without expensive end-to-end training and on hard to get domains. That is embodiments of the present principles advantageously enable the innovative ingestion of unstructured multimodal data on a small scale, such as by using proprietary information to which others do not have access. In accordance with the present principles free form responses to applied multi-level questions enable the implicit knowledge to become explicit knowledge.

As depicted inFIG.1, embodiments of a multi-modal language model adapter of the present principles, such as the multi-modal language model adapter100ofFIG.1, can be implemented in a computing device600in accordance with the present principles. That is, in some embodiments, tasks to be performed, and/or questions intended to be answered using content data, and/or domain-specific multimodal content and the like can be communicated to components of the multi-modal language model adapter100of the embodiment ofFIG.1using the computing device600via, for example, any input/output means associated with the computing device600. Information associated with a multi-modal language model adapter in accordance with the present principles can be presented to a user using an output device of the computing device600, such as a display, a printer or any other form of output device.

For example,FIG.6depicts a high-level block diagram of a computing device600suitable for use with embodiments of a comprehension-based question answering system in accordance with the present principles such as the comprehension-based question answering system100ofFIG.1. In some embodiments, the computing device600can be configured to implement methods of the present principles as processor-executable executable program instructions622(e.g., program instructions executable by processor(s)610) in various embodiments.

In the embodiment ofFIG.6, the computing device600includes one or more processors610a-610ncoupled to a system memory620via an input/output (I/O) interface630. The computing device600further includes a network interface640coupled to I/O interface630, and one or more input/output devices650, such as cursor control device660, keyboard660, and display(s)680. In various embodiments, a user interface can be generated and displayed on display680. In some cases, it is contemplated that embodiments can be implemented using a single instance of computing device600, while in other embodiments multiple such systems, or multiple nodes making up the computing device600, can be configured to host different portions or instances of various embodiments. For example, in one embodiment some elements can be implemented via one or more nodes of the computing device600that are distinct from those nodes implementing other elements. In another example, multiple nodes may implement the computing device600in a distributed manner.

In various embodiments, the computing device600can be a uniprocessor system including one processor610, or a multiprocessor system including several processors610(e.g., two, four, eight, or another suitable number). Processors610can be any suitable processor capable of executing instructions. For example, in various embodiments processors610may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs). In multiprocessor systems, each of processors610may commonly, but not necessarily, implement the same ISA.

System memory620can be configured to store program instructions622and/or data632accessible by processor610. In various embodiments, system memory620can be implemented using any suitable memory technology, such as static random-access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing any of the elements of the embodiments described above can be stored within system memory620. In other embodiments, program instructions and/or data can be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memory620or computing device600.

In one embodiment, I/O interface630can be configured to coordinate I/O traffic between processor610, system memory620, and any peripheral devices in the device, including network interface640or other peripheral interfaces, such as input/output devices650. In some embodiments, I/O interface630can perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory620) into a format suitable for use by another component (e.g., processor610). In some embodiments, I/O interface630can include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface630can be split into two or more separate components, such as a north bridge and a south bridge, for example. Also, in some embodiments some or all of the functionality of I/O interface630, such as an interface to system memory620, can be incorporated directly into processor610.

Network interface640can be configured to allow data to be exchanged between the computing device600and other devices attached to a network (e.g., network690), such as one or more external systems or between nodes of the computing device600. In various embodiments, network690can include one or more networks including but not limited to Local Area Networks (LANs) (e.g., an Ethernet or corporate network), Wide Area Networks (WANs) (e.g., the Internet), wireless data networks, some other electronic data network, or some combination thereof. In various embodiments, network interface640can support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via digital fiber communications networks; via storage area networks such as Fiber Channel SANs, or via any other suitable type of network and/or protocol.

Input/output devices650can, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or accessing data by one or more computer systems. Multiple input/output devices650can be present in computer system or can be distributed on various nodes of the computing device600. In some embodiments, similar input/output devices can be separate from the computing device600and can interact with one or more nodes of the computing device600through a wired or wireless connection, such as over network interface640.

The computing device600can communicate with other computing devices based on various computer communication protocols such a Wi-Fi, Bluetooth® (and/or other standards for exchanging data over short distances includes protocols using short-wavelength radio transmissions), USB, Ethernet, cellular, an ultrasonic local area communication protocol, etc. The computing device600can further include a web browser.

Although the computing device600is depicted as a general purpose computer, the computing device600is programmed to perform various specialized control functions and is configured to act as a specialized, specific computer in accordance with the present principles, and embodiments can be implemented in hardware, for example, as an application specified integrated circuit (ASIC). As such, the process steps described herein are intended to be broadly interpreted as being equivalently performed by software, hardware, or a combination thereof.

FIG.7depicts a high-level block diagram of a network in which embodiments of a multi-modal language model adapter in accordance with the present principles, such as the multi-modal language model adapter100ofFIG.1, can be applied. The network environment700ofFIG.7illustratively comprises a user domain702including a user domain server/computing device704. The network environment700ofFIG.7further comprises computer networks706, and a cloud environment710including a cloud server/computing device712.

In the network environment700ofFIG.7, a multi-modal language model adapter in accordance with the present principles, such as the multi-modal language model adapter100ofFIG.1, can be included in at least one of the user domain server/computing device704, the computer networks706, and the cloud server/computing device712. That is, in some embodiments, a user can use a local server/computing device (e.g., the user domain server/computing device704) to train a language model (e.g., an LLM) to learn tasks for domain-specific multimodal content to adapt the language model to understand and answer questions associated with the domain-specific multimodal content in accordance with the present principles.

In some embodiments, a user can implement a multi-modal language model adapter of the present principles in the computer networks706to train a language model (e.g., an LLM) to learn tasks for domain-specific multimodal content to adapt the language model to understand and answer questions associated with the domain-specific multimodal content in accordance with the present principles. Alternatively or in addition, in some embodiments, a user can implement a multi-modal language model adapter of the present principles in the cloud server/computing device712of the cloud environment710to adapt the language model to understand and answer questions associated with domain-specific multimodal content in accordance with the present principles. For example, in some embodiments it can be advantageous to perform processing functions of the present principles in the cloud environment710to take advantage of the processing capabilities and storage capabilities of the cloud environment710. In some embodiments in accordance with the present principles, a multi-modal language model adapter of the present principles can be located in a single and/or multiple locations/servers/computers to perform all or portions of the herein described functionalities of a system in accordance with the present principles. For example, in some embodiments some components of a multi-modal language model adapter of the present principles can be located in one or more than one of the a user domain702, the computer network environment706, and the cloud environment710while other components of the present principles can be located in at least one of the user domain702, the computer network environment706, and the cloud environment710for providing the functions of the present principles described above either locally or remotely.

The methods and processes described herein may be implemented in software, hardware, or a combination thereof, in different embodiments. In addition, the order of methods can be changed, and various elements can be added, reordered, combined, omitted or otherwise modified. All examples described herein are presented in a non-limiting manner. Various modifications and changes can be made as would be obvious to a person skilled in the art having benefit of this disclosure. Realizations in accordance with embodiments have been described in the context of particular embodiments. These embodiments are meant to be illustrative and not limiting. Many variations, modifications, additions, and improvements are possible. Accordingly, plural instances can be provided for components described herein as a single instance. Boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and can fall within the scope of claims that follow. Structures and functionality presented as discrete components in the example configurations can be implemented as a combined structure or component. These and other variations, modifications, additions, and improvements can fall within the scope of embodiments as defined in the claims that follow.

In the foregoing description, numerous specific details, examples, and scenarios are set forth in order to provide a more thorough understanding of the present disclosure. It will be appreciated, however, that embodiments of the disclosure can be practiced without such specific details. Further, such examples and scenarios are provided for illustration, and are not intended to limit the disclosure in any way. Those of ordinary skill in the art, with the included descriptions, should be able to implement appropriate functionality without undue experimentation.

Embodiments in accordance with the disclosure can be implemented in hardware, firmware, software, or any combination thereof. Embodiments can also be implemented as instructions stored using one or more machine-readable media, which may be read and executed by one or more processors. A machine-readable medium can include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device or a “virtual machine” running on one or more computing devices). For example, a machine-readable medium can include any suitable form of volatile or non-volatile memory.

Modules, data structures, and the like defined herein are defined as such for ease of discussion and are not intended to imply that any specific implementation details are required. For example, any of the described modules and/or data structures can be combined or divided into sub-modules, sub-processes or other units of computer code or data as can be required by a particular design or implementation.

In the drawings, specific arrangements or orderings of schematic elements can be shown for ease of description. However, the specific ordering or arrangement of such elements is not meant to imply that a particular order or sequence of processing, or separation of processes, is required in all embodiments. In general, schematic elements used to represent instruction blocks or modules can be implemented using any suitable form of machine-readable instruction, and each such instruction can be implemented using any suitable programming language, library, application-programming interface (API), and/or other software development tools or frameworks. Similarly, schematic elements used to represent data or information can be implemented using any suitable electronic arrangement or data structure. Further, some connections, relationships or associations between elements can be simplified or not shown in the drawings so as not to obscure the disclosure.

This disclosure is to be considered as exemplary and not restrictive in character, and all changes and modifications that come within the guidelines of the disclosure are desired to be protected.