Floating Cubes

Problem

LLM / GPT AI hallucinates incorrect information

Large Language Models (LLMs) can create incorrect answers to questions that give the impression of being reliable intelligence.

Improved by these Interventions

Integrate a Large Language Model (LLM) AI with a Knowledge Graph

Connect a Large Language Model AI service with a knowledge graph so that the information source is scoped and accurate. This provides a reliable information repository for using the LLM in a specific business area.

Technologies used

Machine Learning (ML)

Large Language Model (LLM) AI

Retrieval-Augmented Generation (RAG) with LLMs

Knowledge Graphs

Deploy a RAG LLM stack with a Knowledge Graph

Use a Knowledge Graph as the source for information retrieval to provide the LLM with correct, scoped, and contextual information.

Technologies used

Machine Learning (ML)

Artificial Intelligence (AI)

Large Language Model (LLM) AI

Retrieval-Augmented Generation (RAG) with LLMs

Integrate ChatGPT with a Knowledge Graph

Integrate the popular LLM AI "ChatGPT" with a Knowledge Graph, so that an organization can use its own scoped and structured knowledge to provide the reference information for the AI.

Technologies used

Machine Learning (ML)

Large Language Model (LLM) AI

KGaaS - Knowledge Graph as a Service

ChatGPT - the LLM AI Product by OpenAI

Knowledge Graphs

Inhibits these Capabilities

Ability to view realtime actionable intelligence

Data Language regularly delivers the capability for staff, partners, and all business users to use live, real-time intelligence data.

Caused by these Problems

LLM / GPT AI is not trained on complete, accurate information

Related Articles

LLMs and Knowledge Graphs - Data Language Spring 2023 Hack Event

LLMs and Knowledge Graphs - Data Language Spring 2023 Hack Event

Integrate a Large Language Model (LLM) AI with a Knowledge Graph

We held a hack event to explore how we might use LLMs to help with common Knowledge Graph customer use cases.