Compressed Context Representations for Generative Model Adaptation
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Solution Overview
Problem
Existing generative models struggle with efficiently adapting to new context data and personalization requirements, leading to high computational costs and latency due to the need for re-training or fine-tuning and lengthy prompts.
Innovation Solution
A system using trained compression models to generate an aggregated compact representation of content items, which is then processed by a generative neural network to adapt to new context without re-training, reducing computational costs and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative models are re-trained or fine-tuned to adapt to new context data, then the model can generate more accurate and personalized responses, but computational costs and time increase significantly
Solution Approach 1:
The patent segments the adaptation process into two distinct components: (1) a compression model that processes context data into compact representations, and (2) a generative model that uses these compressed representations without re-training. This segmentation allows the computationally intensive compression task to be performed once on context data, while subsequent adaptations only require processing new compressed representations, dramatically reducing training time.
Solution Approach 2:
The patent introduces compressed representations as an intermediary between context data and the generative model. Instead of directly feeding raw context data or re-training the generative model, the system uses a compression model to create compact representations that serve as efficient intermediaries. These compressed representations capture essential information while enabling rapid adaptation without triggering expensive re-training operations.
2Adaptability or versatility
If generative models are re-trained or fine-tuned to adapt to new context data, then the model can generate more accurate and personalized responses, but computational costs increase significantly
Solution Approach 1:
The patent segments the adaptation process into two distinct components: (1) a compression model that processes context data into compact representations, and (2) a generative model that uses these compressed representations without re-training. This segmentation allows the computationally intensive compression task to be performed once on context data, while subsequent adaptations only require processing new compressed representations, dramatically reducing training time.
Solution Approach 2:
The patent introduces compressed representations as an intermediary between context data and the generative model. Instead of directly feeding raw context data or re-training the generative model, the system uses a compression model to create compact representations that serve as efficient intermediaries. These compressed representations capture essential information while enabling rapid adaptation without triggering expensive re-training operations.
3Adaptability or versatility
If new content items are incorporated into the context for the generative model, then the model can generate more accurate and personalized responses, but the prompt length increases leading to higher computational costs
Solution Approach 1:
The patent extracts essential information from context data by generating compressed representations that capture the most important features while discarding redundant details. This extraction process transforms lengthy context data into compact representations, allowing the system to maintain personalization capabilities while keeping prompt lengths manageable and computationally efficient.
Solution Approach 2:
The patent fundamentally changes the parameter representation of context data by transforming it from raw, high-dimensional data into compressed, low-dimensional representations. This parameter transformation maintains the essential information needed for personalization while dramatically reducing the size and complexity of the input prompts to the generative model.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a task. In one aspect, a method comprises: receiving a query for a task to be performed; receiving a plurality of context content items for the task; for each content item of the plurality of content items, processing an input comprising a representation of the content item using a trained compression model to generate a compressed representation of the content item comprising one or more vectors of a fixed size; generating, using the compressed representations, an aggregated compressed representation comprising one or more vectors that represents the plurality of content items; and processing an input comprising (i) the query and (ii) the aggregated compressed representation using a generative neural network to generate a response to the query.