Compressed Context Representations for Low-Latency Generative Adaptation
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Solution Overview
Problem
Existing generative models face high computational and training costs when adapting to new context data, particularly due to the need for retraining or fine-tuning and the inefficiency of incorporating new data through prompts, which increases latency and resource usage.
Innovation Solution
A system using trained compression models to generate an aggregated compact representation of context data, allowing efficient adaptation by updating the generative model without retraining, through techniques like mean pooling and self-attention layers, reducing computational costs and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative models are retrained or fine-tuned to adapt to new context data, then the model can generate more accurate and personalized responses, but the computational costs and training time increase significantly
Solution Approach 1:
The system segments the context data into individual data items and processes each through a compression model to generate separate compressed representations. These segmented representations are then aggregated into a unified context representation, enabling efficient adaptation without full model retraining.
Solution Approach 2:
A compression model acts as an intermediary between the raw context data and the generative model. This intermediary transforms context data into compressed representations that can be efficiently integrated without requiring the generative model to process the full original data, reducing computational overhead.
2Adaptability or versatility
If new context data items are incorporated into the generative model through prompts, then the model can adapt to new data, but the prompt length increases and causes higher latency
Solution Approach 1:
The system extracts the essential information from context data items through compression, separating the critical contextual meaning from the full original data. This extracted compressed representation is then used for model adaptation, avoiding the need to include entire context items in prompts.
Solution Approach 2:
The system transforms the context data from its original format into a compressed representation format with reduced dimensions. This parameter change in data representation allows the same amount of contextual information to be conveyed in a more compact form, reducing prompt length and latency.
3Reliability
If the generative model processes all original context data items, then it can maintain high accuracy, but the computational resources and processing time increase
Solution Approach 1:
The compression model performs preliminary processing of context data before it reaches the generative model. By pre-compressing the context items into condensed representations, the system prepares the data in advance, allowing the generative model to work with smaller, pre-processed inputs without losing accuracy.
Data Source
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.