Predictive Query Completion for Low-Latency Generative Output
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Solution Overview
Problem
Generative models, such as large language models, incur significant computational expense and latency when processing user queries, leading to user-perceived delays in generating responses.
Innovation Solution
A system that preemptively generates predicted completion text based on user input using a language model, allowing initial processing of the input and predicted text, reducing the need for full generative model processing and conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full generative model processing is performed on user queries, then processing accuracy is improved, but user-perceived latency increases and computational resources are consumed
Solution Approach 1:
The system performs preliminary processing of the user query using a language model to generate predicted completion text and initial predicted output before the user even finishes typing. This preliminary action allows the system to prepare processing results in advance, so that when the user submits the complete query, the generative model can continue from the already-computed intermediate results rather than starting from scratch, significantly reducing user-perceived latency while maintaining processing accuracy
Solution Approach 2:
The patent segments the query processing into distinct phases: (1) preliminary processing of the input query to generate predicted completion text, (2) initial generation of predicted output using the language model, and (3) final processing using the generative model. This segmentation allows different processing stages to be performed at different times and with different computational resources, optimizing both speed and accuracy
2Measurement precision
If full generative model processing is performed on user queries, then processing accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs partial processing of the query using the language model to generate predicted completion text and initial predicted output, rather than performing the complete processing that would be required if using only the generative model. This partial action consumes fewer computational resources while still achieving accurate results, as the language model handles the preliminary processing that would otherwise require the more resource-intensive generative model
Solution Approach 2:
The patent uses the language model to create a preliminary copy or approximation of the processing results (predicted completion text and initial predicted output) that can be used as a starting point for the final generative model processing. This copying approach allows the system to avoid redundant computations and reduce overall computational resource consumption
Data Source
AI summary
Various implementations include reducing latency when interacting with a generative model system based on generating predicted complete text based on natural language (NL) text input, where the NL text input is a portion of a user query. In many implementations, predicted completion text can be generated by processing NL text input using a language model. In several implementations, the system can perform initial processing of the NL input text and the predicted completion text (e.g., preform initial preprocessing of the NL input text and predicted completion text for processing using the generative model, performing an initial limited decoding of output using the generative model, etc.). The user can confirm the predicted completion text before the system continues processing the NL input and predicted completion text using the generative model to generate output.