KBLaM: Knowledge Base augmented Language Model
Xi Wang, Liana Mikaelyan, Taketomo Isazawa, James Hensman·October 14, 2024
Summary
KBLAM integrates external knowledge into pre-trained language models by transforming an unstructured corpus into a structured knowledge base. It maps knowledge triples into fixed-length key-value vectors, or knowledge tokens, which are seamlessly incorporated into the LLM's attention mechanism using a scalable, rectangular attention structure. This approach allows for efficient, linear scalability with respect to the number of triples, unlike in-context learning's quadratic overhead. KBLAM's design enables direct inspection of knowledge token usage through attention scores and supports learning linear adapters through instruction tuning on synthetic data, facilitating generalization to real-world scenarios.
Introduction
Background
Overview of pre-trained language models (LLMs)
Importance of external knowledge in enhancing LLM performance
Objective
To present KBLAM, a method for integrating external knowledge into pre-trained language models
Highlighting the key features and benefits of KBLAM
Method
Data Collection
Sources of unstructured corpora for knowledge extraction
Techniques for collecting and preparing data for knowledge base creation
Data Preprocessing
Steps involved in transforming unstructured data into structured knowledge triples
Methods for converting knowledge triples into fixed-length key-value vectors (knowledge tokens)
Knowledge Base Transformation
Process of mapping knowledge triples into knowledge tokens
Integration of knowledge tokens into the LLM's attention mechanism using a scalable, rectangular attention structure
Scalability and Efficiency
Explanation of linear scalability with respect to the number of triples
Comparison with in-context learning's quadratic overhead
Attention Mechanism Enhancement
Description of how KBLAM's attention mechanism benefits from the inclusion of knowledge tokens
Discussion on the impact on model performance and efficiency
Implementation
Knowledge Token Usage Inspection
Techniques for directly inspecting the usage of knowledge tokens through attention scores
Analysis of how attention scores reflect the influence of external knowledge
Instruction Tuning
Methodology for learning linear adapters through instruction tuning on synthetic data
Explanation of how this facilitates generalization to real-world scenarios
Generalization and Application
Discussion on the adaptability of KBLAM to various domains and tasks
Case studies or examples demonstrating the effectiveness of KBLAM in real-world applications
Conclusion
Summary of KBLAM's Contributions
Recap of KBLAM's key features and benefits
Future Directions
Potential areas for further research and development
Outlook on the impact of KBLAM on the field of language modeling
Advanced features