- 1st Dec
- 3 min read
Transforming Businesses with Generative AI through Social Entities
In today's competitive landscape, organizations are striving to integrate AI capabilities that enhance productivity and confer a competitive advantage. The Large Language Model (LLM) stands as a groundbreaking tool, steering organizations toward an AI-centric paradigm, transforming the way they leverage technology for increased efficiency and innovation. However, it grapples with challenges, notably the hallucination problem, especially when words have different meanings under various contexts and are used by different people i.e. Polysemy
What is Hallucination and What Causes The Problem?
Hallucination refers to cases where the model delivers responses that while seemingly consistent, are incorrect. In the context of semantic search using LLM, this problem occurs when words assume different meanings in various contexts and can be interpreted differently by different users. These hallucinations can compromise the precision and dependability of search results, potentially leading to miscommunication or misunderstanding. Hallucinations occur because tokens and vectors don't comprehend meaning; instead, they operate on statistical probability.
Top 3 Issues Stemming from AI Hallucination
Mitigating Hallucination: Enhancing Semantic Search Accuracy in Large Language Models
To address this challenge, developers and researchers may employ advanced techniques, such as refining contextual understanding algorithms, incorporating more nuanced contextual clues, or implementing context-aware filters. By fine-tuning the LLM to better discern the contextual nuances of words and phrases, organizations can enhance the model's accuracy in delivering personalized and contextually relevant search results. Additionally, ongoing research and updates to the LLM's training data can help improve its semantic comprehension, reducing the occurrence of hallucination and ensuring more reliable and meaningful user experiences in AI-driven operations.
The Solution!
SONN: Revolutionizing Data Classification for Large Language Model
While LLM serves as a robust foundation for data connectivity and insights, it is not without its challenges. Complex relationships, data quality assurance, and interoperability concerns pose significant hurdles in accurate relationship identification and semantic understanding within LLM. These limitations can impact the accuracy and efficiency of AI-driven solutions, necessitating a strategic approach to address these challenges.
To address the limitations and challenges posed by LLM, we introduce the Social Object Neural Network (SONN). SONN represents a paradigm shift in data classification, leveraging a five-class taxonomy—People, Things, Services, Locations, and Activities—to streamline data organization and relationship management. By implementing SONN, organizations can unlock the full potential of AI-centric operations, driving efficiency and innovation in their workflows.
Implementing SONN in LLM: The integration of SONN into LLM involves a structured approach to data modeling, schema design, metadata annotations, and relationship definitions. By mapping existing data entities to the five-class taxonomy and updating entity structures to align with the defined classes, organizations can streamline data access and relationship identification within the Microsoft ecosystem. SONN offers a clear framework for interpreting data relationships and associations, enhancing semantic understanding and driving operational efficiency.
Use Case Scenario: Employee Resource Management System
LLM without SONN
1. Current Approach:
* Data Entities: Employees, Departments, Projects, Tasks.
* Relationships:
* Employees are associated with Departments.
* Employees work on Projects assigned to them.
* Projects consist of Tasks assigned to employees.
2. Steps: a. Employee Search:
* Prompt LLM for employee details.
* Retrieve employee data along with department, project, and task information.
3. b. Project Allocation:
* Assign a new project to an employee.
* Update project details in the system.
4. Challenges:
* Complex Relationships: Navigating relationships between employees, departments, projects, and tasks can be intricate and require extensive querying.
* Semantic Clarity: Ambiguity in how data entities are related may lead to challenges in interpreting and utilizing the data effectively.
* Data Organization: Without clear classification, managing and accessing data across multiple entities can be cumbersome.
Use Case of LLM Working with the Five Classes Taxonomy:
1. Enhanced Approach with Five Classes Taxonomy:
* Classes: People (Employees), Things (Projects), Location (Departments), Activities (Tasks), Services (Assignment).
* Relationships:
* Employees belong to Departments (Location).
* Projects (Things) are assigned to Employees (People).
* Activities (Tasks) within Projects.
* Assigning (Services) the Tasks.
2. Steps: a. Employee Search:
* Prompt LLM for employee details within the People class.
* Retrieve associated (People), (Things), (Location), (Activities) and (Service) information for each employee.
3. b. Project Allocation:
* Create a new project (Thing) and Tasks (Activity) and assign (Service) to an employee (Person) within the relevant Department (Location).
* Update project details in the system.
4. Advantages:
* Clear Data Classification: Data entities are categorized into distinct classes, simplifying data organization and relationship identification.
* Structured Relationships: Defined relationships within each class facilitate efficient navigation and querying of data connections.
* Improved Semantic Understanding: The taxonomy enhances semantic interpretation by providing a structured framework for data relationships.
Comparison:
* Efficiency: The taxonomy-based approach streamlines data access and relationship identification, reducing complexity and enhancing semantic clarity in managing data entities.
* Scalability: With well-defined classes, scaling the system and adding new data entities becomes more manageable and structured.
* User Experience: Users can navigate and interact with data more intuitively, leading to improved usability and productivity in accessing information.
By applying the five classes taxonomy in LLM, organizations can optimize data management, streamline relationship identification, and enhance semantic understanding within their systems, ultimately improving operational efficiency and user experience.
As explained above the current LLM is limited by tokens based to generate response, and is totally unable to capture the complex data relationships leading to inefficiencies in employee allocation and project management. With SONN, a clear classification into five distinct classes simplifies data access and relationship identification. Resulting in improved semantic understanding and structured data navigation driving operational efficiency, enhancing productivity and streamlining workflows.
SocialNavigator and Large Language Model Integration
User prompts are taken in, and the SocialNavigator (processing agent) utilizes the Social Objects Graph and the semantic index for necessary computations. Later, the processing agent presents the revised query to the Large Language Model and gathers the model's response, followed by tapping into the Social Objects Graph and semantic index for additional refining. Ultimately, the SocialNavigator returns the final response and the associated AI agent command
Configure SocialNavigator
The SocialNavigator index facilitates organizations in incorporating both internal and external data or content. SONN Graph connectors allow the integration of external data sources, enhancing your organization's AI experience by providing relevant responses and access to new digital services. Microsoft indexes all Graph connectors data while ensuring access controls for content. Third-party data, whether hosted on-premises or in public/private clouds, is consumed by the SONN Graph. This information can then be ingested into the semantic index, offering comprehensive organizational context.
SocialNavigator Index updates
After the initial completion of the SocialNavigator semantic index, subsequent interactions between users and entities of the five classes are indexed in near real-time, ensuring immediate updates for any changes.
Each time the users interacts, the scan of 'features' example coffee will add the weightage (see Gp 2). AI response is about accuracy and SONN provides the accuracy
Here's a breakdown of its functions:
User Prompt Processing: SocialNavigator receives user prompts as input.
Semantic Index and Graph Access: It accesses the Social Objects Graph and semantic index to enhance understanding and processing of the user prompts.
Modification of Prompts: The prompts are modified by SocialNavigator before being sent to the Large Language Model (LLM). This modification could involve contextual adjustments or additional information from the accessed graphs.
Interaction with Large Language Model (LLM): SocialNavigator communicates with the Large Language Model by sending the modified prompt and receiving the LLM response.
Post-Processing: After receiving the LLM response, SocialNavigator further processes the information by accessing the Microsoft Graph and semantic index for post-processing.
Response and Command Handling: SocialNavigator then sends the final response and any necessary app commands back to the system.
Security Measures: All interactions involve encryption by HTTPS, and the user's data remains encrypted at rest, ensuring a secure communication channel.
In summary, SocialNavigator acts as a middleware that facilitates the flow of information between user prompts, various graphs, the Large Language Model, and the final response/command stage, while maintaining a focus on security through encryption.
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