Abstract: A golf club head with a face component with a stiffening member is disclosed herein. The golf club having products of inertia Ixy and Ixz with absolute values below 100 g-cm2 and a positive product of inertia Iyz is disclosed herein. The golf club has a large volume, a high moment of inertia, or both. Preferably, the moment of inertia, Izz, of the golf club head about a vertical axis Z through the center of gravity ranges from 3000 g-cm2 to 5000 g-cm2, and a moment of inertia, Iyy, about a horizontal axis Y through the center of gravity of the golf club head is greater than 1900 g-cm2.

Abstract: Examples provide record retention to content items in a content management system. The system identifies a content item stored in a content items database and displays a user interface (UI) that includes a list for a categorization criterion. User input is received from the UI indicating a user selection for the first categorization criterion. A retention mapping schedule maps the criterion options to retention labels of a retention schedule. A retention label for the content item is determined based on the first user selection and the retention mapping schedule, the retention label being defined in the retention schedule and having an expiration duration defining how long content items marked with the retention label are retained. The retention label is stored as metadata for the content item, and the content item is deleted after an expiration duration of the content item has elapsed.

Abstract: A method for using a large language model to perform complex table question-answering tasks while preserving data security and privacy with respect to the underlying data included in a table is provided. The method includes: receiving a user query that relates to a table; generating, based on the user query, a prompt that includes a role, at least one instruction, schema, and a question; transmitting the prompt to an external processor that does not have access to the underlying data included in the table; receiving, from the external processor, a set of executable code which has been generated by the external processor in response to the prompt and which is applicable to the table; generating an answer to the user query by executing the set of executable code; and transmitting, to the user, the answer to the user query.

Abstract: A method for using a large language model (LLM) to generate executable code in a manner that preserves privacy and confidentiality of proprietary data is provided. The method includes: receiving information that relates to a summarization of a data table; defining a set of rules for facilitating a generation of executable code by an LLM; receiving an inquiry from a user; inputting each of the summarization information, the set of rules, and the inquiry into the LLM; receiving, in response to the input, a set of executable code that is generated by the LLM; and executing the set of executable code in order to generate an output. The summarization information may be received from an external source that has access to proprietary data included in the data table, and the summarization information may be structured so as to preserve a privacy of the proprietary data.

Abstract: Various methods and processes, apparatuses or systems, and media for using large language models (LLMs) to extract information from regulatory filings and to aggregate the extracted information in order to generate structured responses to user queries are disclosed. The method includes: receiving a query from a user; using a first LLM to transform the query into a set of action items; using a second LLM to generate plans for responding to each action item; using a third LLM to select an optimum plan for executing the set of action items; using a fourth LLM to assemble a set of fifth LLMs to act as expert LLM sub-agents to perform respective tasks based on the optimum plan; obtaining, from each expert LLM sub-agent, a respective output that results from a performance of the corresponding task; and aggregating the outputs into a single response to the query.

Abstract: A method and system for training an encoder classifier model in predicting hallucination of a machine learning (ML) model before a generation of a query. The method includes implementing a plurality of independent large language models (LLMs) that each perturb a plurality of training queries for a predetermined number (n) of times, wherein the plurality of LLMs comprises n+1; utilizing the plurality of LLMs in performing functions including: generating a perturbed output for each of the plurality of training queries by perturbing the plurality of training queries; sampling an initial training query and each of the perturbed outputs; and deriving empirical probability estimations of hallucinations via a computational statistical simulation on the sampled outputs; and training the encoder classifier model based on the derived empirical probability estimations that predicts the hallucination in the ML model associated with the query before the generation of the query.

Abstract: Various methods and processes, apparatuses or systems, and media for automatic table identification and extraction in a document by utilizing one or more processors along with allocated memory are disclosed. The processor receives a variably sized document and streams content of the variably sized document line by line in a sliding window to identify breakpoints. The streaming is independent to the number of tables in the document, or length of the document, and the breakpoints identify start and end of a table. The processor also identifies and extracts a table within the document based on the breakpoints; implements spatially aware parsing algorithm for layout analysis, table constructions, and radial context search from the identified table; and automatically structures the table in structured triplets of index, column, and value that dictates a row, a column, and an entry value, respectively.

Abstract: An ink cartridge receiving module having a plurality of receiving spaces (14) for receiving a plurality of ink cartridges (20), each receiving space (14) having a bottom surface (22) which is inclined at an angle between 10° and 45° with respect to a horizontal plane, each receiving space (14) further having a connector (30) adapted for removing ink from the respective cartridge (20), the connectors (30) being arranged at the lower side of the receiving spaces (14).

Abstract: A method and a system for improving a quality of software code generated by using a large language model (LLM) via code guardrails are provided. The method includes: receiving a request for performing a task; providing the request as an input to the LLM; receiving, from the LLM, a first set of executable code that is intended to be usable for performing the task; automatically executing the first set of executable code in an environment that includes at least one guardrail component that is configured to detect errors; detecting at least one error, such as a hallucination error, based on a result of the execution; determining at least one feedback item based on the at least one error; and prompting the LLM to generate a second set of executable code based on the request, the first set of executable code, and the at least one feedback item.

Abstract: Various methods and processes, apparatuses or systems, and media for deterministically deriving underlying graph structure and associated text information in a document are disclosed. A processor implements a vision-based algorithm and a network-based algorithm that may extract and structure a diagram from an image obtained from the document. The processor deterministically derives underlying graph structure and associated text information in the document by applying the vision-based algorithm and the network-based algorithm, thereby allowing encoding of graph content and reasoning into downstream applications including LLM inputs, graphical question-answering, and information extraction tasks. The processor also implements OCR algorithm for text fields, and then isolates which piece of text belongs to which node by examining the spatial coordinates of the text against bounding box of the node and executes cross-page resolution.

Abstract: A method and system for generating a dynamic graph network for a plurality of documents in a corpus are disclosed. The method includes analyzing a first document among the plurality of documents included in the corpus; generating a first node for the first document in the dynamic graph network; extracting one or more values included in the first document; assigning a key for each of the one or more values included in the first document; identifying at least one entity for the one or more values extracted from the first document; generating a second node for the at least one entity in the dynamic graph network; setting a status identifier for each of the first node and the second node generated; generating a first edge for the first node and the second node; and establishing a connection between the first node and the second node via the first edge.

Abstract: Various methods and processes, apparatuses or systems, and media for predicting probability of hallucination before generation for a query imposed to a Large Language Model (LLM) are disclosed. A processor causes a trained generative model to receive a query from a user via a user interface operatively connected to the generative model; perturbs the received query n times into unique variations that retain the original semantic meaning of the received query yet significantly diverge lexically; implements n+1 independent agents to sample an output from each query including the original received query; applies the simulation algorithm on the sampled outputs; derives an empirical estimate into an expected rate of hallucination for the original received query as a ground truth for the encoder; and outputs a probability of hallucination value for the query received by the generative model before the LLM generates an output.

Abstract: A method and a system for preparing a response to a subpoena by using an automated subpoena processing and handling model is provided. The method includes: receiving a first subpoena; extracting a set of informational requests from the first subpoena; retrieving a data set from a memory; analyzing the first data set with respect to the set of informational requests in order to identify items from within the data set that are responsive to items included within the set of informational requests; and generating a report that includes a result of the analysis. The analysis is performed by providing the first subpoena as an input to a Robustly optimized Bidirectional Encoder Representations from Transformers pre-training approach (RoBERTa) model that uses a sub-word approach to modeling words in a sequential format.

Abstract: A method for using a large language model to generate executable code for workflow execution in a manner that integrates user feedback and adjusts the workflow as needed while preserving data privacy is provided. The method includes: receiving first information that relates to a workflow context, second information that relates to at least one application programming interface (API), and third information that relates to a code generation request; using the received information to generate a lecture, and transmitting the lecture to a language model; receiving a user query that relates to performing a task, and transmitting the query to the language model; receiving a workflow that is automatically generated by the language model based on the lecture and the query; and executing the workflow in order to generate an output that is responsive to the query.

Abstract: Examples provide record retention to content items in a content management system. The system identifies a content item stored in a content items database and displays a user interface (UI) that includes a list for a categorization criterion. User input is received from the UI indicating a user selection for the first categorization criterion. A retention mapping schedule maps the criterion options to retention labels of a retention schedule. A retention label for the content item is determined based on the first user selection and the retention mapping schedule, the retention label being defined in the retention schedule and having an expiration duration defining how long content items marked with the retention label are retained. The retention label is stored as metadata for the content item, and the content item is deleted after an expiration duration of the content item has elapsed.