Abstract: Various methods and processes, apparatuses or systems, and media for performing a link prediction of a missing node in a knowledge graph are disclosed. The present disclosure provides acquiring the knowledge graph and extracting a set of information from the knowledge graph, the set of information indicates data entities and edges. Each of the edges indicates a relationship between a pair of data entities. The method further includes identifying triplets included in the knowledge graph, in which each of the triplets includes a pair of nodes connected by an edge, and generating an ontological graph model based on the triplets identified and the acquired knowledge graph. The method further performs, via the LLM, a link prediction for the missing node of the knowledge graph based on the generated ontological graph model.

Abstract: A method and a system for detecting an anomalous sequence of events in stream data are provided. The method includes: receiving a first set of raw data; analyzing the first set of raw data in order to determine a first event sequence; applying a first Hidden Markov Model (HMM) to the first event sequence in order to generate a first output; and determining, based on the first output, whether the first event sequence is classifiable as being an anomalous event sequence. The HMM is trained by using known sequences of normal events and event sequences that are known to be anomalous.

Abstract: A method and a system for using a large language model (LLM) to automatically translate textual instructions into executable software code via skill distillation and composition are provided. The method includes: receiving a request for performing a task and a prompt; providing, as an input to an LLM, the first request and a response to the prompt; receiving, from the LLM, a set of code that implements a function that corresponds to a skill that is usable for performing the task; generating a test that relates to the task; performing the test by executing the set of code and checking whether the task has been successfully completed; and when the task has been successfully completed, storing the set of code in a skills library. Sets of code stored in the skills library may then be accessed and combined in order to perform larger tasks.

Abstract: A method and system for verifying a structured query language (SQL) query are provided. The method includes: receiving a first request to retrieve first data that is accessible via a database; identifying an intention of the first request; generating, based on the first request, a first SQL query to retrieve the first data from the database; predicting, based on the generated first SQL query, an output of the generated first SQL query; determining whether the predicted output matches the identified intention of the first request; generating, based on the determining of whether the predicted output matches the identified intention of the first request, a second SQL query to retrieve the first data that is accessible via the database, when the predicted output of the first SQL query does not match the identified intention of the first request.

Abstract: A method and a system for obtaining an evaluation of a quality of software code that is generated by using a large language model (LLM) are provided. The method includes: receiving a set of instructions for performing a task and generating an output; providing, as an input to an LLM, a list of available application programming interfaces (APIs) and the instructions, together with a submission of a request to the LLM to select one API and to generate a set of executable code based on the instructions; receiving, from the LLM, a selection of one API and the set of executable code; executing the set of executable code in order to perform the first task and generate the output; and evaluating an accuracy, a robustness, and/or a consistency of the set of executable code.

Abstract: A method and system for performing a task requiring a plurality of sequential decision-making operations by an autonomous agent are disclosed. The method includes acquiring a portion of offline data from a database, and discovering a set of separate skills. The method further includes segmenting the offline data and segmenting the offline data according to skill, and abstracting the segmented offline data into reusable functions. From the abstracted reusable functions, distilling primitives for each skill and distilling reusable tips in various functions. Modifying skill based text-based and code-based policies for augmenting the autonomous agent, and processing a task requiring a set of sequential decision-making operations using the augmented autonomous agents.

Abstract: A method and a system for detecting an anomalous sequence of events in stream data are provided. The method includes: receiving a first set of raw data; analyzing the first set of raw data in order to determine a first event sequence; applying a first Hidden Markov Model (HMM) to the first event sequence in order to generate a first output; and determining, based on the first output, whether the first event sequence is classifiable as being an anomalous event sequence. The HMM is trained by using known sequences of normal events and event sequences that are known to be anomalous.

Abstract: A method and a system for automated masking of targeted categories of information in resume documents in order to reduce potential implicit biases are provided. The method includes: receiving a resume document; retrieving categories that correspond to types of information to be masked from a memory; extracting, from the resume document, information that belongs to the categories; masking the extracted information; and outputting a modified version of the resume document that includes the masking.