Large-Scale LLM Personalization Using Privacy-Preserving User Embeddings
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Solution Overview
Problem
Large language models (LLMs) often provide generic template responses that fail to incorporate user context, leading to inefficient computational processes when lengthy structured context is included, and retraining for individual users is computationally unfeasible.
Innovation Solution
A method that determines user-specific embeddings using user features, allowing the LLM to generate personalized responses by conditioning on these embeddings while keeping the model parameters fixed, and optionally incorporating local context and accessing personal data repositories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If lengthy structured context is incorporated into the LLM prompt to provide personalized responses, then response personalization is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts only the essential user context information needed for personalization and transforms it into a compact embedding representation. Instead of incorporating lengthy structured context into the prompt, the system extracts user features and converts them into a condensed embedding that captures the essential personalization information in a computationally efficient format.
Solution Approach 2:
The patent transforms user context from a textual/structured format into a numerical embedding representation. This parameter transformation allows the model to process user-specific information efficiently through vector operations rather than dealing with lengthy text, thereby maintaining personalization while improving computational efficiency.
2Adaptability or versatility
If full model retraining is performed to achieve personalized responses for individual users, then response personalization is improved, but computational cost and time deteriorate
Solution Approach 1:
The patent segments the personalization task from the full model training process. Instead of retraining the entire LLM for each user, the system separates the user-specific adaptation into an independent embedding generation component. This segmentation allows the main model to remain fixed while only the embedding generation needs to be performed, dramatically reducing computational requirements.
Solution Approach 2:
The patent performs preliminary processing of user information by pre-computing user embeddings from user features before the actual inference process. This preliminary action transforms raw user data into a ready-to-use embedding representation that can be directly integrated with the LLM prompt, eliminating the need for time-consuming full model retraining.
3Productivity
If generic template responses are used by the LLM, then computational efficiency is improved, but response quality and user relevance deteriorate
Solution Approach 1:
The patent introduces user embeddings as an intermediary between the user context and the LLM processing. These embeddings serve as a mediator that carries user-specific information into the model without requiring full context incorporation or retraining. The intermediary embedding enables the LLM to generate personalized responses while maintaining computational efficiency.
Data Source
AI summary
A method for providing personalized responses to textual prompts using a large scale, privacy preserving, large language model (LLM) includes receiving a textual prompt from a user specifying a task for an LLM to perform, and obtaining a set of user features associated with the user. The method also includes determining, using the set of user features associated with the user, a user prompt embedding for the user, and processing, using the LLM, the textual prompt conditioned on the user prompt embedding for the user to generate a personalized response to the textual prompt. The method further includes providing the personalized response to the textual prompt for output from a user device associated with the user.