Merge-Sort LLM Prompting for Large-Dataset Analytics
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Solution Overview
Problem
Existing online systems struggle to perform advanced analytics on large datasets using large language models (LLMs) due to context window limitations, leading to unsatisfactory user experiences or high human labor requirements.
Innovation Solution
Implement a merge-sort approach to partition data recursively and use merge prompt templates to synthesize outputs from LLMs, enabling efficient processing of large datasets beyond the context window limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the entire large dataset is passed to the LLM for analysis, then the analysis can be performed comprehensively, but the context window of the LLM is exceeded causing the system to fail
Solution Approach 1:
The patent divides the large dataset into multiple smaller subsets using a merge-sort approach. Each subset is small enough to fit within the LLM's context window, allowing the system to process the entire large dataset by recursively splitting it into manageable chunks that can be individually analyzed by the LLM.
Solution Approach 2:
The patent uses a merge prompt template that combines the results from multiple LLM analyses of different subsets into a single comprehensive output. This merging mechanism allows the system to aggregate findings from multiple context window-sized portions to produce analysis equivalent to processing the entire dataset at once.
2Reliability
If the dataset is split into smaller subsets for LLM analysis, then the context window limitation is overcome, but the processing complexity and number of API calls increases
Solution Approach 1:
The patent implements a recursive merge-sort algorithm that systematically divides the dataset into subsets. This segmentation strategy, combined with a standardized merge prompt template, automates the complex process of splitting and combining results, reducing the operational complexity despite increasing the number of processing steps.
Solution Approach 2:
The system uses a self-contained merge prompt template that automatically synthesizes results from multiple LLM responses without requiring manual intervention. The template handles the merging logic internally, allowing the system to manage its own complexity through automated prompt-based synthesis rather than requiring complex external coordination.
3Productivity
If basic generic analytics are used, then the system can automatically respond quickly, but the analytics do not meet the specific needs of the third-party entity
Solution Approach 1:
The patent employs a dynamic prompt template that adapts to different analysis requests from third-party entities. The template can be configured with different analysis types, metrics, and parameters based on the specific needs of each entity, allowing the system to maintain both speed and customization capability through flexible prompt-based configuration.
Data Source
AI summary
An online system enables large language models (LLMs) to perform analytics on large datasets that exceed the LLM's context window by employing a recursive merge-sort prompting approach. Upon receiving a free-text analysis request from a user, the system generates an initialization prompt for the LLM to create a merge prompt template. The dataset is recursively divided into portions, which may be equal or semantically segmented subsets, and each portion is analyzed by the LLM. If a portion still exceeds the context window, the recursive process continues on its subsets. Outputs from each analysis are merged using a merge prompt template, which may include fields for outputs, data, and descriptions of subsets. The merge prompt template is used to generate a merge prompt, which is provided to the LLM to synthesize a final response.