Summary of Kblam: Knowledge Base Augmented Language Model, by Xi Wang et al.
KBLaM: Knowledge Base augmented Language Model
by Xi Wang, Taketomo Isazawa, Liana Mikaelyan, James Hensman
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Knowledge Base augmented Language Model (KBLaM), a novel approach to augmenting Large Language Models (LLMs) with external knowledge. KBLaM combines a pre-trained sentence encoder with linear adapters to transform the knowledge base into continuous key-value vector pairs, which are then integrated into the LLM using a specialized rectangular attention mechanism. Unlike existing methods, KBLaM eliminates external retrieval modules and scales computationally with the size of the knowledge base rather than quadratically. The approach enables integrating a large knowledge base into an 8B pre-trained LLM on a single A100 80GB GPU, allowing for dynamic updates without model fine-tuning or retraining. Experiments demonstrate KBLaM’s effectiveness in various tasks, including question-answering and open-ended reasoning, while providing interpretable insights into its use of the augmented knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KBLaM is a new way to make language models smarter by adding external information. Imagine having access to a huge library where you can find answers to any question. This paper shows how to create a special kind of “library” that can be used with large language models. This helps the model answer questions more accurately and make sense of new information. |
Keywords
» Artificial intelligence » Attention » Encoder » Fine tuning » Knowledge base » Language model » Question answering
