Description:FIELD OF THE INVENTION
The present invention relates to the field of machine learning and natural language processing. More particularly, the invention pertains to a system and method for the continuous fine-tuning of Large Language Models using organizational data. within private deployments. The invention leverages secure data integration, dynamic model updates, and efficient fine-tuning pipelines to improve the contextual relevance and reduce hallucinations in model inferencing, ensuring accurate and real-time responses based on updated organizational information.
BACKGROUND OF THE INVENTION
With the rise of advanced machine learning models, Large Language Models (LLMs) have become a cornerstone of natural language processing, enabling automation across a variety of applications, including content generation, conversational systems, and workflow optimization. Traditionally, fine-tuning these models involved static processes that required large-scale retraining with predefined datasets, limiting their ability to adapt to dynamic organizational needs.
Organizations increasingly demand systems that enable continuous adaptation of LLMs using domain-specific data, while ensuring security and privacy. Existing fine-tuning methods fail to meet this demand, often relying on external, generic data that lacks the contextual relevance needed for accurate responses. Additionally, these methods do not offer the ability to integrate real-time updates, leaving models susceptible to generating irrelevant or hallucinated outputs.
The present invention addresses these challenges by introducing a secure, continuous fine-tuning system for LLMs, specifically designed for private deployments. This system integrates organizational data dynamically and incrementally fine-tunes the model, ensuring it remains contextually relevant while safeguarding data privacy.
Prior attempts to address fine-tuning challenges have introduced innovative approaches but remain incomplete in addressing continuous updates, contextual relevance, and data security within private deployments.
For instance, CN116226334B, this patent describes a method for training large language models using reinforcement learning and user preference data. While it improves user experience in search-based applications, it does not address the dynamic integration of organizational data or context-based fine-tuning within private environments.
US11769017B1, this invention focuses on generating natural language summaries for search results by incorporating additional content. While it mitigates inaccuracies in search results, it is limited to search scenarios and does not provide a continuous fine-tuning mechanism for domain-specific data in organizational deployments.
Likewise, US12112131B2, this patent describes methods for factual extraction using pre-trained language models. While it improves factual accuracy, it does not include mechanisms for integrating real-time organizational data or addressing the need for continuous fine-tuning within secure environments.
Similarly, US11977854B2, this invention relates to enhancing the output of large language models by using structured data in universal machine-readable formats. While it improves text output through context enrichment, it does not support ongoing, incremental fine-tuning of LLMs using organizational data.
However, existing methods are limited in their ability to dynamically integrate organizational data or continuously fine-tune LLMs within private deployments. These approaches lack mechanisms for real-time updates, contextual relevance, and data security. Hence there is a need for a novel system and method for continuously fine-tuning LLMs using dynamic organizational data, ensuring secure deployments, reduced hallucinations, and improved contextual relevance.
DEFINITIONS
"Large Language Models (LLMs)" refers to a class of machine learning models that are designed to process and generate human-like text, typically through the use of deep learning architectures. These models are trained on extensive textual datasets and are capable of performing various natural language processing tasks, such as text generation, summarization, and language translation.
"Continuous Fine-Tuning" refers to the process of iteratively refining a pre-trained model, such as a large language model (LLM), by incorporating new or updated data. This fine-tuning process ensures that the model adapts to new information and remains relevant and accurate over time.
"Organizational Data" refers to data generated, collected, and stored within an organization. This data may include structured and unstructured information such as documents, source code, project management data, tickets, knowledge base entries, and other forms of data generated in the course of the organization’s operations.
"Private Deployments" refers to the deployment of systems or models within an organization’s own infrastructure, such as a private cloud or an on-premises data center. Such deployments ensure that organizational data remains within the organization’s control, protecting confidentiality and security.
"Data Bus Module" refers to a system component that facilitates the integration and communication of data from various organizational sources. It serves as an intermediary to collect, aggregate, and transmit data from diverse repositories and systems to the fine-tuning process.
"Context-Graph Module" refers to a component of the system that organizes and maintains the relationships between different data entities within the organizational data. These relationships are captured using a graph-based structure, allowing for the contextual relevance of data to be preserved.
"Theme and Intent Packets" refers to data representations that encapsulate specific themes and intents derived from organizational data. These packets are structured formats that summarize key information from various sources, such as documentation, source code commits, and project management data, and are used as input for model fine-tuning.
"Pipeline for Fine-Tuning" refers to a series of interconnected processes or stages that prepare, curate, and summarize organizational data for the purpose of fine-tuning an LLM. The pipeline ensures that the data is appropriately processed, organized, and transformed into a format suitable for model training.
"Inferencing" refers to the proc ess of using a trained machine learning model to generate outputs or predictions in response to input data. In the context of this invention, inferencing involves generating contextually relevant and accurate responses based on the fine-tuned model.
OBJECTS OF THE INVENTION
The primary objective of the invention is to provide a system and method for the continuous fine-tuning of large language models (LLMs) using organizational data, ensuring secure deployment within private environments.
Another objective of the invention is to provide a system and method to minimize hallucinations and enhance contextual relevance during inferencing by dynamically integrating updated organizational data.
A further objective of the invention is to provide a system and method to ensure seamless integration with a variety of organizational data sources, including code repositories, ticketing systems, and documentation platforms, to maintain data consistency and relevance.
Yet another objective of the invention is to provide a system and method for summarizing, curating, and utilizing theme-based examples to fine-tune LLMs, ensuring continuous learning and model optimization.
An additional objective of the invention is to implement a mechanism for automatically updating the system in response to changes in organizational data, ensuring that the model remains up-to-date and contextually accurate.
SUMMARY OF THE INVENTION
Before the present invention is described, it is to be understood that the present invention is not limited to specific methodologies and materials described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention.
The present invention discloses a system for the continuous fine-tuning of large language models (LLMs) deployed in private organizational environments, addressing the challenges of traditional static fine-tuning processes. This system integrates dynamic organizational data, ensuring contextual relevance and reducing inaccuracies during model inferencing. At the core of the system is a data bus module, which collects and processes information from diverse organizational sources, including repositories, ticketing systems, and documentation platforms.
A context-graph module maintains dynamic relationships between data entities, enabling the system to understand the broader organizational context. The fine-tuning pipeline curates, summarizes, and processes theme-based examples, creating targeted data sets for efficient model refinement. The continuous tuning mechanism applies advanced methods to incrementally fine-tune the model, ensuring adaptability without requiring complete retraining.
A dynamic update process identifies changes or deletions in the organizational data and adjusts the LLM in real-time, maintaining relevance and accuracy. The system includes a monitoring and logging component to track performance, identify errors, and optimize operations.
By leveraging these components, the system ensures seamless integration of diverse data sources, improved inferencing outcomes, and efficient utilization of computational resources. This innovation is particularly valuable for organizational use cases such as document automation, workflow optimization, and knowledge management, offering a scalable and secure solution for maintaining and enhancing LLMs in complex environments.
BRIEF DESCRIPTION OF DRAWINGS
A complete understanding of the present invention may be made by reference to the following detailed description which is to be taken in conjugation with the accompanying drawing. The accompanying drawing, which is incorporated into and constitutes a part of the specification, illustrates one or more embodiments of the present invention and, together with the detailed description, it serves to explain the principles and implementations of the invention.
Fig1. illustrates the process of continuous fine -tuning systems for large language modules.
Fig2. Features of machine learning and natural language processing.

DETAILED DESCRIPTION OF THE INVENTION
Before the present invention is described, it is to be understood that this invention is not limited to methodologies described, as these may vary as per the person skilled in the art. It is also to be understood that the terminology used in the description is for the purpose of describing the particular embodiments only and is not intended to limit the scope of the present invention. Throughout this specification, the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps. The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the invention to achieve one or more of the desired objects or results. Various embodiments of the present invention are described below. It is, however, noted that the present invention is not limited to these embodiments, but rather the intention is that modifications that are apparent are also included.

No. Name
100 System
110 Data Bus Module
120 Context Graph Module
130 Theme and Intent Packets
140 Pipeline
150 Continuous Update
160 Inferencing
170 End

The present invention relates to an advanced system for the continuous fine-tuning of language models, designed to optimize these models for dynamic and evolving data sources. This system is structured to efficiently manage data ingestion, contextual understanding, and real-time updates for generating high-quality outputs in any application requiring continuous adaptation and improvement. By incorporating different modular components, the system ensures that language models operate with up-to-date information, reflecting the latest developments in the data ecosystem.

The system is composed of several key components that work together to enable real-time integration of incoming data, while preserving the contextual relevance and accuracy of the outputs produced by the language models. These components allow the system to process large volumes of information and adjust the language model continuously, thereby providing highly accurate and relevant outputs without the need for frequent manual intervention or retraining. The system comprises deployment module, data bus module, context graph module and fine tuning module.

Deployment module-
Data Bus Module [110]
The data bus module[110] serves as the central communication hub of the system. It acts as the conduit through which various data sources are connected to the system, ensuring that data flows seamlessly from its point of origin to the relevant modules for processing. The module is designed to handle real-time data streams, enabling the system to receive and incorporate new data as soon as it becomes available. This ensures that the language models are always working with the latest information and are continuously updated.

In addition, the data bus module[110] is responsible for managing the flow of data from internal and external sources, ranging from raw data to highly structured information. It provides a dynamic pipeline for data processing, which is essential for maintaining the relevance and accuracy of the outputs. This module ensures that the language models always operate on a foundation of up-to-date information, regardless of changes in the external environment.

Context Graph Module [120]
The context graph module[120] plays a crucial role in maintaining the relevance of the data being processed. It builds a contextual representation of the data by mapping out relationships between various data points. This allows the system to understand the context in which each piece of information exists, enabling the language model to generate responses or make inferences that are consistent with the overall context.

By organizing data into a network of related points, the context graph module[130] enables the language model to distinguish between different types of data, prioritize information based on its relevance, and adjust outputs accordingly. For example, when new data is ingested, the context graph module[120] ensures that the system understands how this data fits within the broader context of the business, environment, or user requirements. This allows the model to make more accurate predictions, decisions, and responses.

The module is essential for ensuring that the language model does not merely process data in isolation but understands the relationships and dependencies between various pieces of information. This contextual awareness is vital for generating outputs that reflect the true meaning and intent of the data.

Theme and Intent Packets [130]
theme and intent packets[130] are used to refine the way in which the data is categorized and processed. These packets allow the system to classify incoming data according to themes (such as customer feedback, technical documentation, or market trends) and specific intents (such as problem-solving, knowledge discovery, or decision support). This categorization ensures that the language model is able to focus on the most pertinent data while disregarding irrelevant information.

Through the use of theme and intent packets[130], the system can identify and prioritize the key elements of data that align with organizational goals, customer needs, or specific business requirements. By emphasizing the relevant themes and intents, the system ensures that the language model remains focused and optimized for the tasks at hand.

For instance, when new customer feedback is received, the system can categorize it into the appropriate theme (e.g., customer satisfaction) and intent (e.g., identifying service improvement opportunities). This allows the language model to process the feedback more effectively, ensuring that outputs such as automated responses or insights are highly relevant and actionable.

Pipeline [140]
The pipeline[140] component of the system is responsible for managing the flow of data through each of the aforementioned modules, ensuring that each piece of data is properly processed and optimized before being fed into the language model for inference. The Pipeline guarantees that data undergoes the necessary transformations and contextual adjustments before being used to update the language model.

The pipeline[140] ensures that the system operates smoothly and efficiently, enabling continuous updates to the model without disrupting its performance. It coordinates the activities of the data bus module[110], context graph module[120], and theme and intent packets[130], ensuring that data is processed in a systematic and orderly manner. As a result, the language model remains consistently up to date and capable of producing accurate and contextually relevant outputs.

The ability of the pipeline[140] to handle data from a variety of sources in a seamless and efficient manner is essential for the system’s ability to deliver real-time updates and maintain high-quality performance over time. By optimizing the flow of data through the system, the pipeline[140] ensures that the language model remains adaptive and responsive to changing conditions.

Continuous Updates [150]
A distinguishing feature of the system is its capability to continuously update[150] the language model without manual intervention or system downtime. As new data is ingested through the data bus module[110], it is immediately processed and used to fine-tune the language model, allowing it to adjust to changes in context, new information, or shifting priorities.

This continuous update[150] mechanism ensures that the language model stays relevant even as data evolves. The system can update its outputs in real time, providing accurate and timely responses or decisions without the need for periodic retraining. This is particularly important in fast-paced environments where changes occur rapidly, such as in customer service, market analysis, or content creation.

continuous updates[150] are made possible through the interaction of the pipeline[140], context graph module[120], and theme and intent packets, which ensure that the new data is processed, categorized, and integrated into the model seamlessly. As a result, the system can continuously improve its outputs, making it highly adaptive and responsive to new developments.

Inferencing[160]
The inferencing[160] component of the system applies the fine-tuned language model to generate meaningful outputs based on the processed data. Once the data has been ingested, organized, and contextualized, the system uses inferencing techniques to produce intelligent outputs, such as automated responses, decision support, or content generation.

inferencing[160] involves the application of the continuously updated language model to a specific task or query, allowing the system to make informed decisions and deliver accurate responses. The ability to perform real-time inferencing[160] ensures that the system is always aligned with the latest context and data, enabling it to make decisions based on the most current information.

For example, in a customer service environment, the system can generate automated responses to customer queries based on the most recent feedback, ensuring that the responses are accurate and relevant. Similarly, in a technical support environment, the system can infer the best course of action based on the latest available documentation and issue reports.

End [170]
The system's architecture is designed for efficiency and scalability, ensuring that data flows seamlessly through each component, from ingestion to inferencing. The continuous feedback loop, real-time updates, and contextual awareness provided by the modules ensure that the language model remains responsive and accurate over time. The end[170] result is a highly adaptive and effective system that can be used in a variety of industries and applications, from customer service to content generation and beyond.

By integrating real-time updates, dynamic data sources, and contextual modeling, the system provides a powerful solution for maintaining the relevance and accuracy of language models. This invention enables organizations to stay agile and responsive to emerging trends, customer demands, and market conditions, ensuring that their language models are always operating at peak performance.

While considerable emphasis has been placed herein on the specific elements of the preferred embodiment, it will be appreciated that many alterations can be made and that many modifications can be made in preferred embodiment without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the invention and not as a limitation.

, Claims:We claim,
1. A system (100) for the continuous fine-tuning of Large Language Models,
characterized in that,
the system comprises of:
a data bus module (110) configured to integrate and process data from multiple organizational sources, including but not limited to code repositories, ticketing systems, and documentation platforms;
a context-graph module (120) that maintains relationships between data entities, ensuring continuity and relevance in the context of fine-tuning;
a pipeline (140) for fine-tuning that creates, curates, and summarizes theme-based examples for fine-tuning the LLM;
a mechanism for continuous tuning utilizing techniques such as Low-Rank Adaptation to adapt the model incrementally;
a dynamic continuous update (150) process that detects changes, additions, or deletions in organizational data and triggers corresponding model updates; and
improved inferencing (160) results, wherein the fine-tuned LLM provides highly contextual and relevant responses, minimizing hallucinations and ensuring accurate outputs.
2. The system as claimed in claim 1, wherein the data bus module (110) is configured to connect to multiple data sources, including code commits, pull requests, tickets, and project documentation, and processes this data into theme and intent-based packets for use in fine-tuning.
3. The system as claimed in claim 1, wherein the context-graph module (120) employs a knowledge-link approach to represent relationships between various data entities, ensuring that the model’s fine-tuning process accounts for these relationships in its inferencing process.
4. The system as claimed in claim 1, wherein the pipeline (140) for fine-tuning involves generating theme-based examples, summarizing them using pre-and post-change scenarios, and feeding them into the fine-tuning pipeline to improve the model’s contextual accuracy.
5. The system as claimed in claim 1, wherein the continuous tuning mechanism utilizes Low-Rank Adaptation techniques to facilitate incremental updates to the LLM, ensuring the model adapts to changes in organizational data without requiring full retraining.
6. The system as claimed in claim 1, wherein the dynamic update (150) process continually tracks changes to organizational data sources and triggers the necessary model adjustments to maintain high contextual relevance and minimize errors during inferencing.
7. The system as claimed in claim 1, wherein the improved inferencing (160) results reduce hallucinations and out-of-context responses by incorporating the latest organizational data into the fine-tuning process, ensuring that the model's outputs are highly contextual and relevant.
8. The system as claimed in claim 1, wherein the data bus module (110) collects and processes organizational data from multiple systems, such as version control systems, project management platforms, and knowledge repositories, and converts the data into structured packets that are used for theme-based fine-tuning.
9. The system as claimed in claim 1, wherein the context-graph module (120) enables the identification of interdependencies and relationships between data entities, facilitating fine-tuning that preserves contextual meaning across various data types.
10. The system as claimed in claim 1, wherein continuous fine-tuning Large Language Models comprises the steps of:
a. collecting data from multiple organizational sources, including code repositories, ticketing systems, and documentation platforms;
b. processing the data into theme and intent-based packets that are relevant to the fine-tuning objectives;
c. creating a context-graph that maps relationships between different data entities to ensure contextual relevance during model training;
d. generating theme-based examples by summarizing project changes and processing them into inputs for the fine-tuning pipeline;
e. applying low-rank adaptation technique to adapt the model incrementally, allowing for continuous fine-tuning without requiring full retraining;
f. updating the model continuously in response to changes, additions, or deletions in organizational data;
g. improving inferencing results by ensuring that the model provides highly contextual and relevant responses during use.