Personalized Retrieval-Augmented Generation for Account-Specific Accuracy
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Solution Overview
Problem
Existing retrieval-augmented generation systems (RAGs) suffer from inaccuracies due to overgeneralized knowledge bases and lack of adaptability to specific user accounts, leading to unreliable and irrelevant responses.
Innovation Solution
A personalized retrieval-augmented generation system that utilizes a personalized embedding model, vector database, and large language model to generate responses tailored to specific entities by fine-tuning and adapting to their data contexts and user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide-ranging database with vast amounts of data across huge variety of topics is used to train large language models, then broad coverage of output generation is achieved, but accuracy deteriorates due to overgeneralization and hallucinations
Solution Approach 1:
The patent segments the monolithic training approach into two distinct phases: pre-training on broad data for general capabilities, followed by task-specific fine-tuning on targeted data for accuracy. This segmentation allows the model to maintain broad coverage while achieving high accuracy in specific domains through separate training stages.
Solution Approach 2:
The patent applies local quality by using different data sources and training strategies for different aspects of model performance. General knowledge comes from broad pre-training data, while task-specific accuracy is achieved through fine-tuning on curated, domain-specific datasets. This creates models with different quality characteristics for different functional requirements.
2Device complexity
If a one-size-fits-all framework is used for RAG systems, then device complexity is reduced, but adaptability to specific user accounts deteriorates
Solution Approach 1:
The patent implements dynamics by making the RAG system adaptable through configurable parameters and user-specific fine-tuning. While the core framework remains static and simple, the system can dynamically adapt to specific user accounts by loading different training datasets and adjusting model parameters, achieving customization without increasing fundamental system complexity.
Solution Approach 2:
The patent uses copying by creating user-specific model instances through fine-tuning copies of the base model on user-specific data. Each user account can have a customized model copy trained on their particular dataset, allowing the same framework to serve multiple users with different requirements without increasing the base system's complexity.
3Productivity
If existing RAG systems generate responses based on overgeneralized knowledge, then response generation speed is maintained, but response relevance to specific entities deteriorates
Solution Approach 1:
The patent applies preliminary action by performing task-specific fine-tuning before actual response generation. The model is pre-adapted to specific tasks and domains through fine-tuning on relevant datasets, so that during inference it can generate relevant responses quickly without needing to search through broad general knowledge during the response generation process itself.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating personal responses through retrieval-augmented generation. In particular, the disclosed systems can generate a query embedding from a query generated by an entity and determine data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity. The disclosed systems can provide the data context to a large language model and generate a personalized response informed by the data context. Subsequently, the disclosed systems can provide the personalized response for display on a client device associated with the entity.