Token Generation Neural Network for Cached Follow-Up Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models, particularly deep neural networks, are computationally inefficient when processing long contexts due to repetitive processing of the same context for each query, leading to high resource consumption and latency in generating follow-up responses.
Innovation Solution
A system that uses a token generation neural network to pre-generate predicted query-response pairs for a context, processing the context only once to reduce repetitive processing and minimize resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the token generation neural network processes the context for each query separately, then the model can generate accurate responses for each query, but the computational resources and processing time increase significantly
Solution Approach 1:
The system pre-processes the context into a compressed representation (e.g., summary, key points, or embedding) before actual query processing. This preliminary action allows the model to retrieve and utilize essential context information efficiently without re-processing the entire context for each query, thus reducing computational resources while maintaining response accuracy.
Solution Approach 2:
The system extracts only the relevant portions of the context that are needed for query processing. By identifying and separating essential information from redundant data, the model can process queries using only the necessary context segments, significantly reducing the computational burden while preserving response quality.
2Reliability
If the system processes the context repeatedly for each query, then the model can maintain up-to-date understanding, but the response latency increases
Solution Approach 1:
The system performs preliminary context processing and compression before query arrival. By preparing a condensed context representation in advance, the model can quickly retrieve and process only the necessary information when queries arrive, maintaining accurate context understanding while dramatically reducing response latency.
Solution Approach 2:
Instead of processing the original context repeatedly, the system creates a compressed copy or summary of the context that captures essential information. This copy can be rapidly retrieved and processed for multiple queries without re-analyzing the full context, thus maintaining understanding while reducing latency.
3Adaptability or versatility
If the token generation neural network processes long contexts for each query, then the model can handle detailed information, but the processing efficiency decreases
Solution Approach 1:
The system segments the long context into manageable units (e.g., paragraphs, sections, or semantic blocks) and processes only the relevant segments for each query. This segmentation allows the model to handle detailed information effectively while improving processing efficiency by avoiding unnecessary analysis of irrelevant context portions.
Solution Approach 2:
The system extracts and processes only the essential information from long contexts, separating meaningful data from redundant content. By focusing processing on extracted key information rather than the entire context, the model maintains adaptability to detailed information while significantly improving processing efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for pre-generating predictions of subsequent queries with corresponding responses relating to a context using a token generation neural network. In one aspect, a system comprises receiving an input comprising a context and a first query related to the context, processing a model input comprising the context and first query using a token generation neural network to generate a first response to the first query and k predicted query-response pairs, wherein each predicted query-response pair comprises (i) a predicted query that is a prediction of a subsequent query submitted by the user related to the context query and (ii) a corresponding response to the predicted query, providing the first response to the first query for presentation to a user, and caching any of the k predicted query-response pairs.