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External Memory for LLMs
External memories, sometimes referred to as datastores, are independent components that operate alongside a Large Language Model (LLM) to supply extensive contextual information. Built upon established memory-based machine learning techniques, these systems facilitate language modeling by retrieving relevant external data to augment the model's capabilities.
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References
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Internal Memory in LLMs
External Memory for LLMs
A team of engineers is developing a system to help a language model process an entire book. Their approach involves storing the book's text in a separate, searchable vector database. When a user asks a question about the book, a retrieval mechanism first finds the most relevant paragraphs from the database and then provides only those paragraphs to the language model as context to generate an answer. How would this approach to managing long-term information be best classified?
You are evaluating different strategies designed to help a language model process information beyond its standard context window. Match each described strategy to the correct classification of memory model.
Architectural Design for a Knowledge-Base Chatbot
Learn After
Using Retrieved Context to Improve Attention
Retrieval-Based Methods as a Solution for Long-Context Processing
Unsuitability of External Memory for Streaming Contexts
k-NN as a Popular Retrieval-Based External Memory Method
Computational Cost of External Memory Models
Architectural Design for a Real-Time Chat Application
A company is building a question-answering system to help employees query a massive, static knowledge base of over 100,000 internal documents. The core language model has a fixed input size that is much smaller than the total size of the knowledge base. Which approach is the most effective and scalable for ensuring the model can access the necessary information to answer specific user queries accurately?
Evaluating the Use of External Memory Systems for LLMs
Augmented Input Formula for External Memories
Comparison of External Memories in LLMs