Dynamic Content-Addressable Memory Updates for Accurate LLM Outputs
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Solution Overview
Problem
Large language models (LLMs) generate hallucinations, leading to misinformation and biased outputs, which undermine trust and hinder their adoption in certain domains.
Innovation Solution
Augmenting LLMs with addressable external memory units that mimic neocortex-hippocampus interactions, enabling dynamic memory updates and addressing, allowing for targeted memory modifications without altering the LLM, thus preventing hallucinations and reinforcing accurate outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used to generate phrases and sentences, then productivity is improved, but reliability deteriorates due to hallucinations and misinformation
Solution Approach 1:
The patent introduces an external memory system as an intermediary between the LLM encoder and decoder. This memory system stores factual information and retrieves it during generation, acting as a mediator that provides accurate information to the LLM without requiring changes to the LLM's core architecture. The memory system includes memory slots that store key-value pairs, where keys are encoded from input text and values contain factual information that can be retrieved to prevent hallucinations.
2Reliability
If external memory units are added to LLMs, then reliability is improved by preventing hallucinations, but device complexity increases
Solution Approach 1:
The patent segments the memory system into distinct functional components: an encoder that processes input text to generate memory keys, memory slots that store key-value pairs, and a retrieval mechanism that fetches relevant information. This segmentation allows each component to be optimized independently and simplifies the overall integration with the LLM, as each segment performs a specific function in the memory management process.
3Adaptability or versatility
If memory update commands are implemented, then adaptability is improved by allowing dynamic memory modifications, but device complexity increases
Solution Approach 1:
The patent implements a universal memory interface that handles multiple operations (read, write, update, delete) through a unified command structure. The memory system can be dynamically updated with new information, modified, or cleared based on input commands, making it versatile for different applications. This universal interface simplifies control compared to implementing separate mechanisms for each memory operation.
Data Source
AI summary
A computerized system includes a generative large language model (LLM). The LLM includes an encoder configured to provide a vector input to a content addressable memory system and a decoder configured to receive a vector output from the content addressable memory system and generate a data output. The content addressable memory system is configured to receive the vector input and generate a vector output based on data contents of the content addressable memory system. The content addressable memory system is configured to respond to receiving at least one memory update command by updating the data contents of the content addressable memory system.