Structured Data Alias Encoding for Lower LLM Latency
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Solution Overview
Problem
Structured data with long, textually verbose elements introduces latency and errors in large language model processing due to increased computational overhead and tokenization requirements.
Innovation Solution
An encoding module replaces verbose elements in structured data with shorter aliases, and a decoding module converts these aliases back to their original form, optimizing token efficiency and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If verbose structured data elements are used, then data completeness and accuracy are improved, but processing latency and computational overhead increase
Solution Approach 1:
The patent segments the data processing pipeline into three distinct stages: encoding (converting verbose data to compact representation), LLM processing, and decoding (converting back to verbose format). This segmentation allows each stage to optimize for its specific function, with the encoding stage reducing token count to minimize processing latency while preserving data accuracy through reversible transformation.
Solution Approach 2:
The patent introduces an intermediary encoding layer that transforms verbose structured data into a compact intermediate representation before LLM processing. This intermediary form acts as a bridge that reduces computational overhead during processing while maintaining data fidelity through bidirectional mapping, thus resolving the contradiction between data accuracy and processing speed.
2Measurement precision
If long identifiers are used in structured data, then data precision and uniqueness are improved, but token count and computational resources increase
Solution Approach 1:
The patent creates a compact copy or alias of the original verbose identifiers during the encoding phase. These aliases maintain the uniqueness and precision properties of the original identifiers but use significantly fewer tokens. The bidirectional mapping ensures that the compact representation can be perfectly reconstructed back to the original precise identifiers after LLM processing.
3Reliability
If verbose structured data is provided to LLM, then data fidelity is improved, but processing cost and resource requirements increase
Solution Approach 1:
The patent changes the parameter of data representation from verbose text form to compact encoded form before LLM processing. This parameter transformation reduces the amount of data the LLM must process, thereby reducing computational resources and energy consumption. The fidelity is preserved because the transformation is reversible and lossless, allowing perfect reconstruction of the original data after processing.
Data Source
AI summary
A computer method for encoding structured data, the encoding comprising substituting one or more data elements within the structured data with corresponding one or more aliases, thereby producing encoded structured data, wherein the corresponding one or more aliases have a shorter tokenized representation than the one or more data elements; providing the encoded structured data to a Large Language Model (LLM); receiving an output from the LLM; and decoding the output to substitute the corresponding one or more aliases with the one or more data elements.