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KLARNA - Language Models and Knowledge Graphs: A Systems Approach

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Lucas Smedley, Product Manager, Klarna Tales from explorations into the synergies of LLMs and Knowledge Graphs.

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Unlocking the Potential of Knowledge Graphs and LLMs: A Systems Approach Lucas Smedley | Applied AI at Klarna
LLMs and knowledge graphs are currently in a state of purgatory Heaven* Hell KGs x LLMs * heaven = production grade applications and systems
Rebuild our knowledge graph stack from the ground up with LLMs embedded throughout, in order to maximise their potential. We must take a systems approach to integrating LLMs with knowledge graphs
● Knowledge is patterns ○ LLMs and patterns ○ Knowledge graphs and patterns ● Current state of integration between knowledge graphs and LLMs ○ The challenges of production use cases ● The future and how we can get there ○ Taking a systems approach ○ Helpful tips Today we will cover:
Knowledge and intelligence is just pattern recognition Knowledge | Current State | Future State
How do we know a dog is a dog? Knowledge | Current State | Future State
Pattern: Four legs, a tail, barking, a wet nose, arguing with a cat. How do we know a dog is a dog? Knowledge | Current State | Future State
How do we know when someone likes us? Knowledge | Current State | Future State
Pattern: Eye contact, making time for you and showing interest. Laughing at our bad jokes. How do we know when someone likes us? Knowledge | Current State | Future State
How do we detect fraud in financial transactions? Knowledge | Current State | Future State
Pattern: Unusual spending patterns; transactions across multiple geographies in a short space of time, transactions at unusual times, transactions on unexpected good or services. How do we detect fraud in financial transactions? Knowledge | Current State | Future State
For LLMs pattern prediction is grounded in predicting the next token accurately. In order to do this, they in turn develop intelligence. LLMs are just large scale pattern recognition and prediction engines Knowledge | Current State | Future State
Knowledge graphs are our opinionated version of the patterns in the world we care about Our knowledge graph schemas, ontologies and taxonomies are effectively the data points and patterns in the world we care about within a certain context. Knowledge | Current State | Future State
Pattern in a regular DB: Dog Cat Knowledge | Current State | Future State
Graphs are much richer: Dog Cat Eats Knowledge | Current State | Future State
Graphs are much richer: Dog Cat Loves Knowledge | Current State | Future State
By focusing on patterns, we can design systems where LLMs and knowledge graphs are interoperable and intrinsically connected. Both technologies are complementary. LLMs are very good at at synthesizing large quantities of unstructured data. Knowledge graphs provide frameworks for structured, easily navigable representations of that information. Knowledge Graphs and LLMs are similar in their focus on patterns Knowledge | Current State | Future State
Knowledge Graphs and LLM’s: The Current State of the Play Knowledge | Current State | Future State
Typically involves turning a natural language query into a structured cypher query, which is executed against a graph. The result is then turned into a natural language answer. Use Case 1: Using LLMs to Query graphs Knowledge | Current State | Future State
Involves taking unstructured information and processing to into more structured information of a knowledge graph. Use Case 2: Using LLM’s to build knowledge graphs Knowledge | Current State | Future State
● Often results don’t work at production scale and complexity graphs But we are encountering a lot issues Knowledge | Current State | Future State
● Often results don’t work at production scale and complexity graphs ● Querying graphs ○ LLMs don’t receive enough information to generate correct cypher queries ○ Schemas have not been designed with querying in mind ○ Infrastructure is not optimised for LLMs But we are encountering a lot issues Knowledge | Current State | Future State
● Often results don’t work at production scale and complexity graphs ● Querying graphs ○ LLMs don’t receive enough information to generate correct cypher queries ○ Schemas have not been designed with querying in mind ○ Infrastructure is not optimised for LLMs ● Building Graphs ○ Examples are often shown with ‘schemaless’ graphs ○ Unable to handle edge cases ○ Unreliable output when populating graphs ○ Generated schemas are of low quality But we are encountering a lot issues Knowledge | Current State | Future State
Current solution space for KGs and LLMs Building Querying Knowledge | Current State | Future State
Building Querying Informs To reach production we need to have a greater focus on building the system Knowledge | Current State | Future State
So how do we get there? Solutions Systems Knowledge | Current State | Future State
Objective: building schemas effectively We should treat our LLM like a new employee in terms of the context we provide Building and populating graphs Knowledge | Current State | Future State
Node 1: * Supplier name: Firenze Inc * Location: Italy Building and populating graphs with the system in mind Node 2: * Supplier name: Firenze Inc * Location: Italy * Desc: Producer of high quality knitwear and wool garments Query: find me a jumper supplier in italy? Knowledge | Current State | Future State
Objective: turn the messy into structured Let’s lean into our better documentation and structured frameworks like Pydantic to do better extraction. Populating graphs Knowledge | Current State | Future State
Objective: use queries and responses to feedback and improve the system Cache queries and cypher and pass as few-shot examples to the LLM Adjust schemas or add descriptions and relationships based on querying difficulties. Querying and improving the system Knowledge | Current State | Future State
Today we covered: ● Knowledge is patterns ○ LLMs and patterns ○ Knowledge graphs and patterns ● Current state of integration between knowledge graphs and LLMs ○ The challenges of production use cases ● The future and how we can get there ○ Taking a systems approach ○ Helpful tips Knowledge | Current State | Future State
● Intro to LLMS ● Pydantic is all you need ● Ilya on Dwarkesh podcast Essential reading: Solutions Systems

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KLARNA - Language Models and Knowledge Graphs: A Systems Approach

  • 1. Unlocking the Potential of Knowledge Graphs and LLMs: A Systems Approach Lucas Smedley | Applied AI at Klarna
  • 2. LLMs and knowledge graphs are currently in a state of purgatory Heaven* Hell KGs x LLMs * heaven = production grade applications and systems
  • 3. Rebuild our knowledge graph stack from the ground up with LLMs embedded throughout, in order to maximise their potential. We must take a systems approach to integrating LLMs with knowledge graphs
  • 4. ● Knowledge is patterns ○ LLMs and patterns ○ Knowledge graphs and patterns ● Current state of integration between knowledge graphs and LLMs ○ The challenges of production use cases ● The future and how we can get there ○ Taking a systems approach ○ Helpful tips Today we will cover:
  • 5. Knowledge and intelligence is just pattern recognition Knowledge | Current State | Future State
  • 6. How do we know a dog is a dog? Knowledge | Current State | Future State
  • 7. Pattern: Four legs, a tail, barking, a wet nose, arguing with a cat. How do we know a dog is a dog? Knowledge | Current State | Future State
  • 8. How do we know when someone likes us? Knowledge | Current State | Future State
  • 9. Pattern: Eye contact, making time for you and showing interest. Laughing at our bad jokes. How do we know when someone likes us? Knowledge | Current State | Future State
  • 10. How do we detect fraud in financial transactions? Knowledge | Current State | Future State
  • 11. Pattern: Unusual spending patterns; transactions across multiple geographies in a short space of time, transactions at unusual times, transactions on unexpected good or services. How do we detect fraud in financial transactions? Knowledge | Current State | Future State
  • 12. For LLMs pattern prediction is grounded in predicting the next token accurately. In order to do this, they in turn develop intelligence. LLMs are just large scale pattern recognition and prediction engines Knowledge | Current State | Future State
  • 13. Knowledge graphs are our opinionated version of the patterns in the world we care about Our knowledge graph schemas, ontologies and taxonomies are effectively the data points and patterns in the world we care about within a certain context. Knowledge | Current State | Future State
  • 14. Pattern in a regular DB: Dog Cat Knowledge | Current State | Future State
  • 15. Graphs are much richer: Dog Cat Eats Knowledge | Current State | Future State
  • 16. Graphs are much richer: Dog Cat Loves Knowledge | Current State | Future State
  • 17. By focusing on patterns, we can design systems where LLMs and knowledge graphs are interoperable and intrinsically connected. Both technologies are complementary. LLMs are very good at at synthesizing large quantities of unstructured data. Knowledge graphs provide frameworks for structured, easily navigable representations of that information. Knowledge Graphs and LLMs are similar in their focus on patterns Knowledge | Current State | Future State
  • 18. Knowledge Graphs and LLM’s: The Current State of the Play Knowledge | Current State | Future State
  • 19. Typically involves turning a natural language query into a structured cypher query, which is executed against a graph. The result is then turned into a natural language answer. Use Case 1: Using LLMs to Query graphs Knowledge | Current State | Future State
  • 20. Involves taking unstructured information and processing to into more structured information of a knowledge graph. Use Case 2: Using LLM’s to build knowledge graphs Knowledge | Current State | Future State
  • 21. ● Often results don’t work at production scale and complexity graphs But we are encountering a lot issues Knowledge | Current State | Future State
  • 22. ● Often results don’t work at production scale and complexity graphs ● Querying graphs ○ LLMs don’t receive enough information to generate correct cypher queries ○ Schemas have not been designed with querying in mind ○ Infrastructure is not optimised for LLMs But we are encountering a lot issues Knowledge | Current State | Future State
  • 23. ● Often results don’t work at production scale and complexity graphs ● Querying graphs ○ LLMs don’t receive enough information to generate correct cypher queries ○ Schemas have not been designed with querying in mind ○ Infrastructure is not optimised for LLMs ● Building Graphs ○ Examples are often shown with ‘schemaless’ graphs ○ Unable to handle edge cases ○ Unreliable output when populating graphs ○ Generated schemas are of low quality But we are encountering a lot issues Knowledge | Current State | Future State
  • 24. Current solution space for KGs and LLMs Building Querying Knowledge | Current State | Future State
  • 25. Building Querying Informs To reach production we need to have a greater focus on building the system Knowledge | Current State | Future State
  • 26. So how do we get there? Solutions Systems Knowledge | Current State | Future State
  • 27. Objective: building schemas effectively We should treat our LLM like a new employee in terms of the context we provide Building and populating graphs Knowledge | Current State | Future State
  • 28. Node 1: * Supplier name: Firenze Inc * Location: Italy Building and populating graphs with the system in mind Node 2: * Supplier name: Firenze Inc * Location: Italy * Desc: Producer of high quality knitwear and wool garments Query: find me a jumper supplier in italy? Knowledge | Current State | Future State
  • 29. Objective: turn the messy into structured Let’s lean into our better documentation and structured frameworks like Pydantic to do better extraction. Populating graphs Knowledge | Current State | Future State
  • 30. Objective: use queries and responses to feedback and improve the system Cache queries and cypher and pass as few-shot examples to the LLM Adjust schemas or add descriptions and relationships based on querying difficulties. Querying and improving the system Knowledge | Current State | Future State
  • 31. Today we covered: ● Knowledge is patterns ○ LLMs and patterns ○ Knowledge graphs and patterns ● Current state of integration between knowledge graphs and LLMs ○ The challenges of production use cases ● The future and how we can get there ○ Taking a systems approach ○ Helpful tips Knowledge | Current State | Future State
  • 32. ● Intro to LLMS ● Pydantic is all you need ● Ilya on Dwarkesh podcast Essential reading: Solutions Systems