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Business-oriented, responsible, white-box Generative AI - Achieve it through synergy with graphs.
UNLOCK THE FULL POTENTIAL OF LLMS
Commonly known challenges of Large Language Models (LLMs), such as their black-box nature, hallucinations, and overly general knowledge base, have severely hindered their practical application in organizations.
LLMs struggle to answer multi-hop questions, such as uncovering connections between historical figures who lived in different times and places.
Graphs are renowned for their interconnected, traceable, and explicit representations, empowering efficient deep traversal and causality searches.
LLMs excel in generating coherent text responses to various inputs but face challenges with even basic mathematical questions.
Graphs, rooted in the principles of graph theory, provide solid analytical and algorithmic frameworks, rendering them reliable problem solvers.
HYBRID SOLUTIONS
LLMs and graphs exhibit inherent complementarity. By harnessing the textual comprehension abilities of LLMs and the structured reasoning power of graphs, their integration opens up compelling opportunities to enhance the capabilities, intelligence, and interpretability of AI systems.
TRANSFORM UNSTRUCTURED DATA INTO GRAPH
Leverage the entity discovery and relation extraction capabilities of LLM to extract information from raw text and directly visualize them in a graph.
ENGAGE WITH DATA CONVERSATIONALLY
LLMs can effectively understand user inquiries in natural language and translate them into precise graph queries or algorithms in UQL.
FROM RELEVANT TO PRECISE
The strategic integration of OpenAI, LangChain, Chroma Vector DB and Ultipa for optimal QA System performance, utilizing graphs for knowledge acquisition through pathfinding queries and node properties.
Read More
While LLMs have showcased remarkable capabilities in understanding and generating natural language, the concerns and criticisms around them have also cast shadows on their applicability in serious business contexts. The synergy between LLMs and graphs holds the promise of mitigating the limitations associated with LLMs. By harnessing the explicit and structured representation of relationships provided by graphs, this harmonious integration anticipates a forthcoming era in data processing and problem-solving. In this envisioned future, the inherent strengths of both technologies will collaborate to overcome existing challenges and obstacles.
