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 response accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of the user query using a language model to generate predicted completion text and initial responses before the user even finishes typing. This advance preparation allows the full generative model to process pre-computed inputs, significantly reducing the time from query completion to final response while maintaining accuracy through subsequent refinement steps.
Solution Approach 2:
The processing pipeline is divided into multiple stages: initial language model prediction, generative model processing of predicted text, and optional refinement. By segmenting the processing into these phases, the system can perform computationally intensive operations on predicted completions rather than raw user input, reducing overall processing time while preserving response quality through multi-stage refinement.
2Measurement precision
If full generative model processing is performed on predicted completion text, then output quality is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial processing by using the language model to generate predicted completion text that captures the essential meaning of the user query. The full generative model then processes only this predicted text rather than the complete original query, achieving sufficient output quality with reduced computational resources. This partial action approach processes enough information to maintain quality while avoiding redundant computation on already-understood portions of the query.
3Measurement precision
If the system waits for complete user input before processing, then processing accuracy is improved, but user-perceived latency increases
Solution Approach 1:
The system begins processing the user query using the language model as soon as the user starts typing, generating predicted completion text and initial responses in advance. This preliminary action continues even as the user is still inputting their query, so that by the time the user finishes, much of the processing is already complete or near-complete, dramatically reducing user-perceived latency while maintaining accuracy through final refinement steps.
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
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.