Fact Augmented Generation
Current Large Language Models (LLMs) often employ a Retrieval Augmented Generation (RAG) technique, utilizing databases to inject context that reduces hallucinations.
Our approach enhances this by creating embeddings from semantically relevant corpuses, stored against distinct concepts within a fact web.
This enables LLMs to navigate the graph, providing accurate, insightful, and contextually appropriate responses across diverse domains and user scenarios.
The goals include improved user satisfaction, enhanced decision-making processes, and the advancement of AI technologies.
- Precise and Relevant Responses: Semantic search functionality allows LLMs to retrieve information directly from the knowledge graph, ensuring precise and relevant responses.
- Contextual Understanding: In-context learning enables better understanding of user queries, leading to more contextually appropriate responses.
- Domain Expertise: Sub-graph-based fine-tuning allows LLMs to specialize in specific domains, enhancing accuracy in related queries.
- Reduced Bias and Errors: Curated knowledge graphs reduce the likelihood of biased or inaccurate responses, enhancing reliability.
- Enhanced User Engagement: Relevant and insightful responses improve user satisfaction and retention.
- Adaptive Learning: Continuous monitoring and adaptation improve responses over time based on real-world usage.
- Efficient Decision-Making: Knowledge inference aids in faster and more accurate decision-making, particularly in critical scenarios.
- Personalization: Tailored responses based on user history and preferences enhance user experience.
- Efficient Learning Curve: LLMs accelerate understanding of complex subjects for users new to a domain or topic.
- Continuous Improvement: Iterative refinement based on user feedback ensures ongoing enhancement.
- Insights Generation: Correlation of information from the knowledge graph enables insights discovery.
- Multilingual and Multidomain Support: Knowledge graphs support various languages and domains, ensuring broad applicability.
The integration of semantic search, knowledge graphs, and advanced machine learning techniques opens exciting possibilities for LLMs like GPT. This approach harnesses knowledge representation through semantic graphs and utilizes in-context learning for fine-tuning. Here's how:
- Data Collection: Crawl structured from reputable Linked Data sources, such as fact.claims.
- Data Preprocessing: Extract meaningful entities and relationships using an LLM.
- Knowledge Graph Curation: Link your retrieved entities and relationships to your existing facts.
- Indexing: Create an index of the knowledge graph for efficient search and retrieval.
- Facts Search: A search interface that turns natural language queries into fact webs.
- User Interactions: Analyze user queries and responses to improve LLM understanding.
- Contextual Expansion: Incorporate relevant entities from the graph into LLM responses.
- Dynamic Learning: Update the LLM model based on user interactions over time.
- Related Concepts Exploration: LLMs can assist users in navigating the knowledge graph by suggesting related concepts to explore deeper. Users can follow these suggestions to delve into specific topics of interest and gather more comprehensive information.
- Monitoring and Evaluation: Continuously monitor user interactions and responses.
- Iterative Refinement: Update the knowledge graph, search algorithm, and fine-tuning strategies based on feedback.
- Ethical Considerations: Regularly review and curate the graph to ensure quality and mitigate biases.
- Sub-Graph Extraction: Identify relevant sub-graphs for specific domains.
- Data Augmentation: Use sub-graphs to augment LLM training data.
- Fine-Tuning: Specialize the LLM in domain-specific information using augmented data.