Token Processing Neural Network With Parallel Tool Calls
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Solution Overview
Problem
Existing neural network systems that rely on auto-regressive architectures and external tools face issues such as lengthy API call roundtrip times and nonresponsive calls, leading to increased latency and reduced performance in generating output sequences.
Innovation Solution
A token processing neural network is configured to generate partial output sequences and external tool requests strategically, postponing tool requests to the end of the sequence and allowing parallel execution of API calls, thereby reducing latency and improving responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network system uses external tools to generate output sequences, then the quality and accuracy of the output can be improved, but the latency increases due to lengthy API call roundtrip times
Solution Approach 1:
The system performs preliminary actions by generating tool requests and executing API calls in parallel before the full output sequence is needed. By preparing tool responses in advance and caching them, the system reduces the latency when the actual output generation is required, thus resolving the contradiction between maintaining high output quality through external tools and reducing the time loss from API roundtrips.
2Reliability
If the system executes API calls sequentially to ensure proper ordering, then the reliability of tool response integration is improved, but the productivity decreases due to increased total execution time
Solution Approach 1:
The system segments the output generation process into independent parts that can be processed in parallel. By dividing the tool requests into separate parallel API calls while maintaining logical separation of their responses, the system can execute multiple tool queries simultaneously rather than sequentially. This segmentation allows the system to maintain reliability through proper response integration while significantly improving productivity by reducing total execution time.
Solution Approach 2:
The system dynamically adjusts the execution strategy by using parallel API calls for independent tool requests while maintaining sequential processing for dependent requests. This dynamic approach allows the system to maximize productivity by parallelizing where possible while preserving reliability through conditional sequential processing when tool responses have dependencies, thus resolving the contradiction between reliability and productivity.
3Measurement precision
If the neural network waits for all external tool results before generating output, then the accuracy of the output sequence is improved, but the user experience deteriorates due to longer waiting times
Solution Approach 1:
The system performs preliminary actions by executing external tool API calls in parallel before the user needs to see the final output. By preparing tool responses in advance and having them ready when the neural network generates the final output sequence, the system maintains high output accuracy while significantly reducing the perceived waiting time for users, thus resolving the contradiction between accuracy and user experience.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output sequences using a token processing neural network and one or more external tools. The output sequences are then presented for display.